Factors affecting the job satisfaction levels of shift ...

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Factors affecting the job satisfaction levels of shift workers in the aviation sector: An

empirical study in New Zealand

A thesis presented in partial fulfilment of the requirements for the degree of

Master of Business Studies

in

Management

at Massey University, Albany, New Zealand.

Safa Chanane

2017

JOB SATISFACTION OF SHIFT WORKERS IN THE NZ AVIATION SECTOR

ii

Abstract

People spend much of their lives at work and job satisfaction significantly contributes to

their well-being. It leads to higher employee commitment, productivity and loyalty, which

in turn contributes to superior organisational performance. Shift work may impact on

employee job satisfaction, especially when organisations split working hours due to 24-

hours service demands. Numerous studies have measured job satisfaction in relation to

personal characteristics, organisational and environmental factors in various contexts.

However, there are conflicting views at the individual level so this study will focus on

individual employee characteristics. Furthermore, little is known about job satisfaction in

the aviation sector despite its significance to the economy and employment of many shift

workers. This research thus focuses on New Zealand aviation shift workers’ job

satisfaction. An online questionnaire explored the impact of personal demographic

variables (e.g. gender, marital status) and risk factors (e.g. stress, isolation) on the shift

workers’ job satisfaction. Survey data were analysed using statistical techniques.

Thematic analysis of qualitative survey material complemented the quantitative findings.

The study revealed that job satisfaction varies according to the departments in which

respondents work. Moreover, there is a low to moderate negative relationship between

job satisfaction and stress, difficulty falling asleep and isolation; and moderate to

substantially negative relationship between job satisfaction and health issues, fatigue and

family conflict. Health issues, fatigue and family conflict were the three main predictors

of job satisfaction. The findings inform implications for theory, practice and policy,

particularly in Human Resource Management, are discussed with recommendations for

further research.


iii

Acknowledgements

Firstly, I would like to thank the staff at Massey University, my supervisor Professor Jane

Parker, for agreeing to supervise the thesis and for her continual guidance, support,

knowledge and encouragement. As well as co-supervisor Associate-Professor Janet

Sayers for her insightful comments and Doctor Nazim Taskin for his guidance with areas

of the data analysis.

Secondly, I would also like to extend my appreciation to those shift workers who

participated within the study; without their time and input, this study would not be

possible. In addition, a sincere gratitude to the managers within the case organisation who

provided their approval and support throughout the study - I hope this study proves

beneficial to the well-being of the case organisation employees.

Lastly, I would like to thank my father, mother, sisters, brother, my dearest nephews

and friends for their endless support, inspiration and care, throughout this challenging

journey. This one is for you.


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Table of Contents

Abstract ii

Acknowledgements iii

Table of Contents iv

List of Tables vii

List of Figure vii

Appendices viii

List of Tables viii

List of Figures viii

Chapter 1 – Introduction 1

Introduction to the Subject 1

Purpose of the Study 7

Research Approach 8

Study Contribution 8

Outline of the Study 9

Chapter 2 – Literature Review 10

2.1 Introduction 10

2.2 Job Satisfaction 10

2.3 Job Satisfaction Assessment 14

2.4 Shift Work 15

2.5 Factors Affecting Job Satisfaction 17

2.6 Research Questions 37

2.7 Summary 39


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Chapter 3 – Research Method 40

3.1 Introduction 40

3.2 Research Purpose 40

3.3 Research Approach Overview 41

3.4 Data Collection Approach 43

3.5 Access and Selection 45

3.6 Ethical Considerations 46

3.7 Target Population 47

3.8 The Survey 49

3.9 Data Collection Procedure 53

3.10 Survey Diagnostics 54

3.11 Data Analysis Approach 58

3.12 Summary 59

Chapter 4 – Data Analysis and Results 61

4.1 Introduction 61

4.2 Contextual Survey Data 61

4.3 Empirical Findings for the First Research Question 63

4.4 Empirical Findings for the Second Research Question 67

4.5 Empirical Evidence for the Third Research Question 71

4.6 Suggestions From Participants 77

4.7 Summary 78

Chapter 5 – Discussion and Conclusion 80

5.1 Introduction 80

5.2 Purpose of the study 80

5.3 Significant Findings 81


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5.4 Implications of the Key Study Findings 84

5.5 Study Contributions 87

5.6 Limitations and Future Research 88

5.7 Summary and Final Remarks 92

References 93

Appendices 122

Appendix A: Access Letter 122

Appendix B: MUHEC Approval Letter 123

Appendix C: Survey Invitation 125

Appendix D: Online Survey 127

Appendix E: Email from Manager to Employees 136

Appendix F: Survey Diagnostics 137


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List of Tables

Table 1: Industries working Evening and Night Shifts (Welch, 2013) ............................ 5

Table 2: Number of Employees in Each Department .................................................... 48

Table 3: Reliability of Job satisfaction........................................................................... 53

Table 4: Frequency Table of Reasons for Working Shifts ............................................. 62

Table 5: Frequency Table of Benefits Outweigh the Drawbacks of Working Shifts .... 63

Table 6: General Linear Model (Tests of Between-Subjects Effects) ........................... 64

Table 7: ANOVA (in Terms of Departments) ............................................................... 65

Table 8: Tukey HSD Multiple Comparisons ................................................................. 66

Table 9: Correlation between Job Satisfaction and the Risk Factors ............................. 69

Table 10: Hierarchical Method of Entry for the Predictor Variables (Variables

Entered/Removeda) ........................................................................................................ 72

Table 11: Model Summary of the Multiple Regression Analysis .................................. 73

Table 12: The Overall Significance of the Multiple Regression Model (ANOVAa) ..... 75

Table 13: The Contribution Measurements of the Predictor Variables to the Multiple

Regression ...................................................................................................................... 76

List of Figures

Figure 1: Personal Demographic Variables ................................................................... 17

Figure 2: Risk Factors .................................................................................................... 29

Figure 3: Initial Conceptual Framework ........................................................................ 38

Figure 4: Developed Conceptual Framework ................................................................ 85

Figure 5: Future Research Framework ........................................................................... 91


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Appendices

List of Tables

Table A1: Early and Late Responses ........................................................................... 137

Table A2: Harman's Single Factor Test (Total Variance Explained) .......................... 138

Table A3: Descriptive Statistics of Overall Job Satisfaction Score ............................. 139

Table A4: Test of Normality for Job Satisfaction ........................................................ 139

Table A5: Test of Homogeneity of Variances (Levene's Test of Equality of Error

Variancesa) ................................................................................................................... 140

Table A6: Multicollinearity Table ............................................................................... 141

List of Figures

Figure A6: Residuals Histogram ................................................................................ 142

Figure A7: P-P Plot of Regression Standardized Residual ......................................... 142


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Chapter 1 – Introduction

Introduction to the Subject

Over recent decades, job satisfaction has been extensively researched in industrial and

organisational psychology, organisational behaviour, management and Human Resource

Management (HRM) (Cooper & Locke, 2000; Zaman & Rahman, 2013). Since 1974,

more than 2,000 studies have been published on job satisfaction across the world

(Dantzker & Surrette, 1997). However, the concern with job satisfaction in research will

not lessen, as long as individuals continue to work.

According to research, job satisfaction is a significant contributor to an individual's well-

being, as he or she spends a substantial amount of his or her adult life in the workplace.

Understanding job satisfaction helps organisations to recognise the factors that drive

employee satisfaction, which in turn improve organisational turnover, productivity and

employee loyalty. Job satisfaction enables an organisation to deliver high performance

and enhance organisational outcomes. It has major impact on organisational success as

high levels of job satisfaction can lead to an increase in productivity, efficiency and

enthusiasm, as well as an inspiration for workers to be more diligent and honest

(Shahidul-Islam, 2016; Spector, 2012) and achieve a higher desire to remain with the

organisation for longer (Sarker, Crossman & Chinmeteepituck, 2003). However, job

satisfaction has an inverse relationship with withdrawal behaviours (such as absence and

turnover) and health (e.g. Castle, Engberg, Anderson & Men, 2007; Davood & Tayebeh,

2012; Mahdi, Zin, Nor, Sakat & Naim, 2012). Therefore, understanding job satisfaction

is important for improving employee well-being at work (and beyond), productivity and

organisational performance.


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1.1.1 Shift work

Shift work is not a modern phenomenon; it has been in existence since the 1800s. Due to

increase in economic demand for around-the-clock service and introduction of artificial

lighting, modern societies now heavily depend on shift work to meet the needs of society

(Strazdins, Korda, Lim, Broom & D’Souza, 2004). Shift work is most simply referred to

as splitting working hours into two or more rotations to cover the time needed to perform

a duty, thus requiring two or more teams of workers (Srivastava, 2010). During the days

of ancient Rome, deliveries of goods were restricted to the night hours to reduce

congestion on the roads, likewise, bakers worked during early hours of the day to prepare

fresh products. During this period, shift work was mainly conducted by male trades and

craft workers, who were able to change trades if they were not able to work shifts. Shift

work gained prevalence during the industrial revolution, where it was impractical for

companies to close down industrial processes and operated 24 hours a day in order to

maintain low operating costs (Monk & Folkard, 1992). Modern lifestyle requires an

instant service from various providers within the health, transport, security and

communication sectors (Pati, Chandrawanshi & Reinberg, 2001).

The number of employees working shifts has increased throughout the years due to

operational requirements and technological advancements. In the service sector, the

majority of businesses require workers around the clock, for example, in health care,

security, police, military, firefighting and airports. Therefore, employees need to be on

shift-work patterns, where their regular working hours are commonly outside standard

hours (0700 to 1900 Monday to Friday) (Folkard, Lombardi & Tucker, 2005; Grosswald,

2004; Knutsson, 2004). Each sector has a unique working environment (e.g. the nature of

the work differs considerably between shift workers in the health sector and those in the


3

food or utility sectors). However, all are under the umbrella of shift work and special

consideration is needed in order to deliver the best to their customers.

1.1.2 Employment and shift work in New Zealand

New Zealand’s workforce consists of over two million individuals, 83.6% are employees,

from which 90% are permanent and the other 10% are temporary employees (casual

workers and fixed-term and temporary agency). Men and women are almost equivalent

in the category, 50.7% and 49.3% respectively (Welch, 2013).

According to the Survey of Working Life in New Zealand (2013), 29.59% of employees

specified that they did not usually work all hours during a standard time (0700 to 1900,

Monday to Friday) (Male=57.40%, Female=42.60%), and a further 4.24% specified that

they have no usual working time (Male=52.80%, Female=47.20%). Of those who did not

usually work during standard times, 51.39% had a spouse or partner which were

employed, 36.9% had no spouse or partner and 11.71% had a partner or spouse who were

not employed. Furthermore, 66% had no dependent children, whereas 14.34% had a

dependent child, 13% had 2 dependent children, 4.48% had three dependent children and

1.69% had 4 or more dependent children (Welch, 2013). In some, the survey identifies

the considerable diversity of shift workers and their circumstances in New Zealand.

Working largely during daytime was the most common work pattern for employed

individuals in New Zealand (87.62%). Of those employed, 3.43% primarily worked

during the evening (1900–2300), 1.31% during night (2300–0500), and 6.74% worked

varying shifts. Furthermore, nearly nine out of 10 employed individuals were either

‘satisfied’ or ‘very satisfied’ with their main occupation, in comparison to one in 20 who

were either ‘dissatisfied’ or ‘very dissatisfied’. Furthermore, eight in 10 employees were


4

either ‘satisfied’ or ‘very satisfied’ with their work-life balance, in comparison to one in

10 who were either ‘dissatisfied’ or ‘very dissatisfied’. In addition, 18.48% of employees

working mostly evening, night or changing shifts, reported that they experienced

difficulties such as sleeping and health (55.38%), family and home responsibility

(29.65%), social, leisure or personal (25.87%), and other types of difficulties including

education and commuting (14.97%). 55.36% of males found work to be stressful at times,

in comparison to 51.45% for females. In addition, physical problems and pain due to work

was another common factor among 58.62% of males and 49.87% of females. 51.51% of

females often found themselves too tired to enjoy life outside work, in comparison to

males where 56.16% were rarely tired to enjoy life outside work (Welch, 2013). This

identifies the significant impact of shift work on employee well-being in New Zealand,

and further empirical research is required based on sectors where shift work levels are

significant.

1.1.3 Industries

The five largest industries in New Zealand are retail trade, accommodation and food

services (14.33% of all employees), manufacturing, electricity, gas, water, and waste

services (11.44%), health care and social assistance (11.27%), education and training

(8.46%), professional, scientific, technical, administrative and support services (8.26%).

The highest proportion of evening and night schedules is reported in the following

divisions: retail, trade, accommodation and food services (32.5%), health care and social

assistance (29.5%), manufacturing and electricity, gas, water and waste services (25.5%),

professional, scientific, technical, administrative and support services (22.3%), transport,

postal and warehousing (15.6%), and education and training (13.3). Table 1 further


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identifies the industry and the percentage of employees which work evening and night

(Welch, 2013).

As of 2013, the transport, postal and warehousing industry is not considered one of the

largest industries in the country (in relation to number of employees). However, it is one

of the industries that highly requires and follows shift schedules. The majority of

researchers acknowledge the high percentage of shift work in most of the listed industries

(Table 1) and have conducted studies within these industries. However, very few have

examined the transport, postal and warehousing industry. Therefore, this research will be

focusing on the aviation sector, which is a significant part of the transport, postal and

warehousing industry.

Table 1

Industries working Evening and Night Shifts (Welch, 2013)

Industry Evening

(%)

Night

(%)

Total

(%)

Retail trade, accommodation and food services 18.2 14.3 32.5

Professional, scientific, technical, administrative

and support services 12.4 9.9 22.3

Health care and social assistance 12.2 17.3 29.5

Education and training 9.3 4.0 13.3

Manufacturing and electricity, gas, water and

waste services 8.9 16.6 25.5

Transport, postal and warehousing 5.4 10.2 15.6

1.1.4 Transport, postal and warehousing industry

According to the Australian and New Zealand Standard Industrial Classification

(ANZSIC) (2017), the transport, postal and warehouse division includes units involved

in passenger and freight transportation by road, rail, water or air. They also provide


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support services such as airport operations and customer agency services (ANZSIC,

2017). In this division, 84.84% of employees are either ‘satisfied’ or ‘very satisfied’,

10.60% are neither ‘satisfied’ nor ‘dissatisfied’ and 4.56% are ‘dissatisfied’ or ‘very

dissatisfied’ (Welch, 2013).

The air transport industry is relatively a new industry, only having emerged in the early

twentieth century, it has established itself as a major contributor to global Gross Domestic

Product (GDP) (Air Transport Action Group, n.d.). It has generated five million direct

jobs worldwide, with 4.3 million people working for airlines and airports globally. The

aviation sector is is one of the greatest contributors to the advancement of New Zealand

society and to the region’s economic growth (New Zealand Government, 2012). It is a

key player in driving growth in travel, tourism and trade sectors in New Zealand (Air

Transport Action Group, 2005).

The case organisation examined in this study belongs to the aviation sector, it provides

air passenger and cargo services domestically and internationally (Case Organisation, 2015).

Due to the services provided and the nature of the sector, workers are required 24 hours

a day, 365 days a year. These workers work various hours of the day to meet operational

requirements and industrial demands of the case organisation. They work in various roles

and departments with a mixture of shift patterns and shift durations. These employees are

the front-line staff and the face of the organisation to their customers. Their level of job

satisfaction is a significant factor to the success of the organisation.

Several researchers (AbuAlRub, 2004; Knutsson, 2004; Pidsrksi, Brook et al., 2006) have

studied the impact of shift work on job satisfaction and other characteristics such as

health, absenteeism, turnover, psychological well-being, familial accord/discord and job

performance. Burch, Tom et al. (2009) identified that shift work leads to a decrease in job


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satisfaction. Shift workers are found to experience issues, such as, job stress, work family

conflict, fatigue, and social isolation. These pose a direct and/or indirect influence on job

satisfaction (Spector, 2012).

Purpose of the Study

Majority of the front-line staff at the aviation sector are shift workers. Their satisfaction

with their job is an important concern for any organisation; it is the main component of

the work environment and the organisational climate (Conley, Bacharach & Bauer, 1989).

After an extensive review of the literature and conflicting views regarding the personal

demographic variables and risk factors which influence employee job satisfaction, this

study will aim to explore the impact of personal demographic variables (i.e. personal

characteristics and personal work related characteristics): department, position,

employment type, gender, marital status, age, educational level, work experience and

number of dependents; so as to deepen understanding of their impact on job satisfaction

among shift workers. In addition, it will look into certain risk factors: health issues,

difficulty falling asleep, family conflict, fatigue, stress and isolation; related to shift work

which eventually impact employees’ job satisfaction in the aviation sector in New

Zealand. In relation to shift workers in the aviation sector, this research will address the

following research questions:

1. Does job satisfaction vary according to personal demographic variables?;

2. What is the nature and extent of the relationship between job satisfaction and the

risk factors?; and

3. Can job satisfaction be predicted by the risk factors?


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Research Approach

Secondary and primary quantitative and qualitative data were collected in this study.

According to Bryman and Bell (2015) primary data are information that is collected by

the researcher via questionnaires or interviews. Secondary data refer to information

gathered from journal articles, books and reports. In this study, primary data were

collected using an online survey. Secondary data, such as organisational website material,

documents and personal correspondence, were mainly used to provide a research context

and help corroborate the primary data.

Several studies have used a quantitative methodology with a global approach to measure

general or overall job satisfaction (Buitendach & Rothmann, 2009). Building on this

quantitative tradition, this research will follow an identical approach (see section 2.3). In

addition, several qualitative elements (open-ended questions) were added to the survey in

order to gain an understanding of underlying reasons and opinions of participants on

certain issues. A single organisation was examined using a cross-sectional questionnaire

as the main research tool. It was distributed and collected through Qualtrics (an online

platform).

Study Contribution

Job satisfaction among shift workers has been given considerable attention in various

contexts and countries. Although the transport, postal and warehousing industry (to which

the aviation sector belongs), in New Zealand has one of the highest proportions of shift

workers. A lack of research in this sector underscores the need for further investigation

in order to provide findings which can inform the improvement of the working

environment and employee well-being, as well as insure smooth progress with

organisational strategic goals. Therefore, this research will contribute to the existing


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literature by filling the gap and extending the existing practitioner understanding of job

satisfaction in relation to shift workers in the aviation sector in New Zealand. The aviation

sector is a significant business sector in New Zealand, and this study’s focus on a case

based at a major airport which is considered a significant asset and contributor to the

country’s economy (see section 1.1.4). The findings will also be of value in guiding policy

makers in designing appropriate strategies to motivate the case organisation employees

to improve the quality of their services to clients. Furthermore, findings could have a

significant theoretical contribution and be of utility to other organisations in the aviation

sector and beyond.

Outline of the Study

This chapter introduced the need for and significance of the study, its scope and key

contributions. Gaps identified in the extant literature surfaced key research questions for

this study and a research methodology was developed. Chapter 2 presents a synopsis of

the job satisfaction theories, followed by a review of the relevant literature on job

satisfaction assessment, job satisfaction among shift workers, factors affecting job

satisfaction (personal demographics and risk factors) in order to provide academic

evidence of the current knowledge, and to demonstrate the rationale for the focus of this

research. It concludes with a statement of the intended contribution that this study will

provide to the extent literature. Chapter 3 describes the quantitative research methodology

with few qualitative elements that is applied in this research. The research design method

and data collection processes are explained and justified. The data analysis and results of

the study are presented in Chapter 4. In Chapter 5, the outcomes of the key research

questions are compared with the literature reviewed. In addition, implications for theory,

practice and policy along with study contributions, limitations and future research are

discussed. The chapter concludes with a summary and final remarks.


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Chapter 2 – Literature Review

2.1 Introduction

This chapter provides an overview and review of the extant literature on the main topics

of the research, job satisfaction and shift work. Definitions of job satisfaction, followed

by theories of job satisfaction, are discussed in Section 2.2. An assessment of job

satisfaction is reviewed in Section 2.3, followed by a review of job satisfaction among

shift workers and reasons for working shifts in Section 2.4. Factors affecting job

satisfaction are explored in Section 2.5, including personal demographic variables and

risk factors. Section 2.6 concludes with clear research questions that derive from gaps in

scholarly work. Section 2.7 provides a summary of the chapter.

2.2 Job Satisfaction

2.2.1 Definition

Researchers and practitioners have given job satisfaction considerable attention,

particularly in terms of its importance and its impact on organisations and their

employees. High levels of job satisfaction have been associated positively with job

performance (Samad, 2005) and organisational commitment (Bytyqi, Reshani & Hasani,

2010), as well as, negatively with withdrawal behaviours (Cooper & Locke, 2000) and

health (Faragher, Cass & Cooper, 2005). It is well established that job satisfaction leads

to organisational success, however emphasis needs to be placed on understanding the

factors which drive job satisfaction among individuals and the risk factors associated with

conducting shift work.

Due to the extensive literature available on job satisfaction, the meaning of the term has

been conveyed differently by researchers. In organisational research, the most common

perspective of job satisfaction is the positive or pleasant emotional state resulting from an


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individual’s appreciation of his or her own job or experience (Locke, 1976; also Zaman

& Rahman, 2013). While others view it as how individuals feel towards their job, such as

Spector (1997), defined job satisfaction as the “extent to which people like their jobs; job

dissatisfaction is the extent to which they dislike them” (p. 210) or expresses employees'

feelings about their work (Arches, 1991). According to Hoppock’s (1935), often cited

definition, job satisfaction is a “combination of psychological, physiological and

environmental circumstances that causes a person to say: I’m satisfied with my job” (p.

47). In addition, Samad (2005) defined job satisfaction as “a contribution of cognitive and

affective reactions to the differential perceptions of what an employee wants to receive

compared with what he or she actually receives” (p. 79). It is evident that there are several

inconsistencies in defining job satisfaction, this is common among literature which is

widely studied and various determinants and scope on job satisfaction, such as personal

issues and/or job characteristics, influences each researcher’s definition.

Given the aforementioned views on job satisfaction, we view job satisfaction as a set of

favourable or unfavourable feelings which employees have towards their jobs within an

organisation. It is widely accepted that job satisfaction reflects the way an employee feels

about his or her employment. All employees experience a degree of satisfaction or

dissatisfaction within their job at some point in their life. Some tend to be more satisfied

with certain aspects of their job compared to others. An individual with a high level of

job satisfaction tends to have a positive attitude, in contrast to one with a lower level of

job satisfaction who tends to have a less positive attitude (Mohanty, 2016) which would

in turn impact on the working environment and co-worker relationships.


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2.2.2 Job satisfaction theories

There are numerous theories that attempt to explain job satisfaction. Three main theories

will be discussed in this section: Adams’ Equity theory (1965); Herzberg’s two-factor

theory (1966); and Hackman and Oldham’s Job Characteristic Model (1979).

Equity theory

Adams (1965) demonstrated that individuals compare their inputs (efforts) and outputs

(rewards) at their job with their friends, colleagues and/or those working in a similar

sector. When an individual determines that they are receiving less output than others or

there is an inequity, they are reportedly becoming dissatisfied, and react by putting less

effort into their work, taking long coffee breaks and reducing their level of input

(Gruneberg, 1979). Inputs comprise the quantity and quality of an individual’s

contributions to his or her work. They include: effort, time, commitment, tolerance,

personal sacrifice and enthusiasm. On the other hand, outputs are the rewards gained in

return for their input, this includes: job security, remuneration, benefits, recognition,

reputation, responsibilities, sense of achievement, commendation and acknowledgements

(Dugguh & Dennis, 2014). Remuneration or salary is the main focus in this theory as

employees want to feel that their efforts are appreciated, so they expect to be paid well.

If an employee feels or perceives that he or she is getting underpaid, this can lead to

potential job dissatisfaction and ultimately results in lack of motivation and low

performance from the employee.

Herzberg’s two-factor theory

Herzberg’s two-factor theory (Motivator Hygiene Theory) is one of the earliest theories

of job satisfaction (Gruneberg, 1979). Herzberg distinguishes between two factors that


13

drive job satisfaction. Firstly, there are motivators (work itself, responsibilities and

achievement) which lead to job satisfaction if they are available in the working condition.

However, its absence does not necessarily lead to job dissatisfaction. Secondly, there are

hygiene factors (e.g. company policies, working conditions and pay) which can

potentially lead to job dissatisfaction when they are insufficient, but do not lead to job

satisfaction when adequate (Gruneberg, 1979). According to Gruneberg (1979), a number

of studies have reached conclusions that support the aforementioned theory. However,

other researchers have proven that this theory is method-bound, meaning that if another

method of data collection were used, the results frequently fail to confirm the theory.

Therefore, the theory has gained little empirical support from other researchers (Spector,

1997).

Job characteristics model

Changing job and task characteristics can make a job more interesting, enjoyable, and

meaningful which in turn enhances level of job satisfaction for an employee (Spector,

2012). Hackman and Oldham’s (1979) job characteristic theory demonstrates how job

characteristics affect an individual. According to their model, if the job provides the five

core characteristics (skill variety, task identity, task significance, autonomy and job

feedback), then it is likely to be more satisfying and motivating than when it does not

provide these characteristics (Cooper & Locke, 2000). Furthermore, the model includes

‘Growth Need Strength’ as a personality variable, which influences the effect of the core

characteristics on the outcome (work motivation, job performance, job satisfaction and

turnover). This theory suggests that employees who are eager to have a challenging and

interesting job will have higher level of job satisfaction and motivation in complex jobs

(Hackman & Oldham, 1979). This theory has been supported by several researchers,

gained significant empirical support, and has been a foundation of several job


14

characteristics instruments such as the Job Diagnostic Survey (1976). However, it has

been criticised by a few researchers as it does not identify the direction of the relationship

or causality between job characteristics and job satisfaction (Spector, 1997).

2.3 Job Satisfaction Assessment

Several researchers have measured the job satisfaction of employees using quantitative

methods (Buitendach & Rothmann, 2009) which are somewhat reflective of the

disciplines (e.g. organisational psychology) that have mostly examined employee job

satisfaction, there is an emphasis on quantitative methods. Two key approaches include

the facet and the global perspectives. The facet approach (multiple-item measure)

measures specific dimensions of job satisfaction. Whereas, the global approach measures

general or overall job satisfaction (Buitendach & Rothmann, 2009; Spector, 2012).

Numerous studies (e.g. Alghamdi, 2015; Dall'Ora, Griffiths, Ball, Simon & Aiken, 2015)

have used the global approach to measure job satisfaction. Building on this quantitative

tradition, this research will follow an identical approach. In contrast, several researchers

have assessed employees’ overall job satisfaction by asking their managers to predict their

employee’s level of job satisfaction. This method is, however, considered to be inaccurate

since the employee is the only person aware of his or her true feelings towards their job

and whether he or she is satisfied with it or not (Spector, Dwyer & Jex, 1988). Although

this method allows the researcher to discover a manager’s perception of an employee’s

level of job satisfaction, it fails to measure employees’ actual job satisfaction. Therefore,

it could be used to compare managers’ perceptions of employees’ job satisfaction while

not accounting for actual employee overall job satisfaction. The following section will

elaborate on job satisfaction among shift workers, in particular, due to its significant use

in the aviation sector.


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2.4 Shift Work

2.4.1 Job satisfaction among shift workers

Several researchers have measured job satisfaction among shift workers in different

contexts (Jaradat, Nielsen, Kristensen and Bast-Pettersen, 2017; Shields, 2006).

However, there was limited literature available within the aviation sector, thus this section

will be reviewed based on available literature on job satisfaction among shift workers.

Jamal and Baba (1992) conducted a study among nurses to compare between permanent

and temporary night shift workers and found shift work to not impact the level of job

satisfaction. However, rotating shifts from a day to night did result in a decrease in the

level of job satisfaction. Furthermore, Jaradat et al. (2017) examined the association of

shift work with mental distress and job satisfaction in relation to gender among

Palestinian nurses using a questionnaire. They reported that men working shifts were less

satisfied with their work than regular day shift workers. In addition, Shields (2006)

reported that employees working during regular day hours were more satisfied than those

who work evenings or night shifts, and employees working rotating shifts were more

satisfied than those working evening or night shifts. However, Moneke and Umeh (2014)

found no statistical significant relationship among shift work and job satisfaction of

critical care nurse in New York. Similar findings were reported by Ruggiero (2005) for

self-defined day, night and rotating-shift nurses.

The conflicting findings could potentially be due to individuals having different abilities

to tolerate shift work. Some may feel alert at particular times of the day and function best

at that time, whereas others may not. For example, Goswami, Jena and Mahapatro (2011)

asserted that shift work accommodates “night owls”. In addition, people have different

degrees of health and fitness levels, use of different coping strategies and/or are able to


16

organise their social and domestic life well, thus being able to manage shift work and feel

satisfied with their job (Costa, 2003; Nachreiner, 1998). However, there are several

reasons why people conduct shift work even though there is criticism of the concept based

on empirical findings.

2.4.2 Reasons for working shifts

Although shift work has various negative effects, employees tend to prefer to work shifts

due to several reasons which in turn contributes to their overall job satisfaction. The

reasons for choosing to work shifts include flexibility in working hours (Awan, 2013;

Finn, 1981); higher income (Awan, 2013; Peetz, Murray & Muurlink, 2012; Rathore,

Sharma, Singh, Tiwari & Sharma, 2015; Shen & Dicker, 2008); lack of supervision from

managers during after-hours (Hood, 1988; Peetz et al., 2012; Rosa & Colligan, 1997);

freedom in working practice and pace (Hood, 1988); time for accepting and pursuing

offered training (Awan, 2013); option for additional study (Rosa & Colligan, 1997);

nature of the job (Rathore et al., 2015; Rosa & Colligan, 1997; U.S. Bureau of Labor

Statistics, 2000); lack of options in jobs available (Peetz et al., 2012; Rathore et al., 2015;

Rosa & Colligan, 1997); and convenience for domestic responsibilities (Peetz et al., 2012;

Rathore et al., 2015; Rosa & Colligan, 1997).

According to the U.S. Bureau of Labor Statistics (2000), 51% of full-time employees

worked shifts due to the nature of their job. A recent study on nurses conducted in India,

found that all employees performed shift work as it was part of their role and the high

income associated with it. Moreover, among younger respondents, 50% mentioned it was

the only job available and 60% stated that it was not convenient for domestic

responsibilities. However, older respondents said it was the only available job (83%) and

it was convenient (66%) (Rathore et al., 2015).


17

Several reasons drive employees to work shifts despite of its negative impact on their

health and well-being. It would be of significant benefit to explore the underlying reasons

which drive employees to work shifts within the New Zealand aviation sector.

2.5 Factors Affecting Job Satisfaction

2.5.1 Personal demographic variables

The scope of this study is to examine personal variables in relation to job satisfaction

among shift workers due to a considerable gap. Figure 1 illustrates a summary of the most

significant variables considered for this study that would impact the level of job

satisfaction among shift workers. These variables will be reviewed in the following

sections based on several sectors and industries. However, there are few studies available

in relation to the aviation context. Furthermore, a number of factors are influential and

work in tandem with one another. The following sections provide a detailed examination

of each whilst recognising its potential impact on each variable. It is important to note

that this list is not limited and can include more variables, however, for this study the

following are only considered.

Figure 1. Personal Demographic Variables

Personal Demographic Variables Department

Position Employment type

Age Educational level Work experience

Marital status Number of dependent children

Level of Job Satisfaction


18

Department

The nature of work performed by an employee within their department has a significant

impact on their level of job satisfaction (Griffin & Moorhead, 2012; Sharma & Bhaskar,

1991). Employees tend to prefer variety and challenge in their jobs which allows them to

use their skills and abilities (Sharma & Bhaskar, 1991). A challenging and interesting job

generally leads to higher levels of job satisfaction (Locke, 1976). In a quantitative study

by Kessuwan and Muenjohn (2010), a T-test and analysis of variance was applied to

analyse the differences among employees’ work and personal variables for their level of

job satisfaction in a multinational corporation in Thailand. They found that employees

experienced a high level of job satisfaction towards the work itself, as their job was

challenging, interesting, enjoyable and allows authority and freedom while performing

their job. However, this study was conducted in a single location, therefore the findings

are not representative for the whole corporation.

Position

Contradictory findings have been found in relation to job satisfaction and work positions.

Howard and Frink (1996) found a positive relationship between job satisfaction and

managerial position. Where employees with a managerial position were more satisfied

than non-managerial employees. This is further supported by Clark (1997) and Kawada

and Otsuka (2011), who found that employees with managerial responsibilities were more

satisfied than general workers. However, Gigantesco, Picardi, Chiaia, Balbi and Morosini

(2003) and Kessuwan and Muenjohn (2010) found no significant statistical difference

between job satisfaction and hierarchy positions.

In terms of shift work, executives were found to be more satisfied than non-executive

employees as concluded by Goswami et al. (2011) in a quantitative study among five


19

ferro-alloy industries in India to explore the effect of work related problem on shift

workers’ attitude, aptitude and job satisfaction. However, the generalisability of these

findings to different sectors could be questioned, as different sectors have different work

requirements and nature of work varies significantly.

Employment type

Globally, organisations tend to hire employees to work for less than 40-hours a week in

order to reduce extra costs associated with employee benefits (e.g. medical insurance and

airline tickets). However, missing out on benefits could potentially lead to a decrease in

an employee’s level of job satisfaction. This could be a result of the part-time employee

performing the same job or task as a full-time employee, but, without receiving similar

benefits (Spector, 2012).

Several researchers have sought to understand the differences in behaviour and attitude

among full-time and part-time employees (Conway & Briner, 2002). Understanding these

differences is vital given their effects on organisational behaviour (Thorsteinson, 2003).

Studies comparing job satisfaction among two groups of employments concluded

contradictory findings. In a quantitative comparative study of full-time and part-time

employees, Eberhardt and Shani (1984) found that part-time employees were more

satisfied than full-time employees, and that this could be due to their expectations about

their job. Similar results were found by Levanoni and Sales (1990). However, it was also

determined that part-time employees were less satisfied (Miller & Terborg, 1979;

Clinebell & Clinebell, 2007). Nonetheless, Logan, O‘Reilly III and Roberts (1973)

concluded that there is no difference in the level of satisfaction between full-time and

part-time employees in a hospital in the United States. A meta-analysis study with a

sample size of 51,213 participants conducted by Thorsteinson (2003) confirmed the


20

findings, with no difference found among full-time and part-time employees and job

satisfaction.

Shields (2006) examined dissatisfied full-time and part-time shift workers in relation to

gender across Canada, however, they did not disclose the industry or sector the study was

conducted in. It was found that men working part-time were more dissatisfied than those

working full-time. In contrast, women working full-time were more dissatisfied than

those working part-time. A possible explanation identified within the study was that men

working part-time possibly dislike having to share household duties. Whereas, women

working full-time were possibly physically and/or mentally exhausted towards the end of

the day, therefore, they prefer to work part-time as it enables them to balance between

work and home responsibilities. Working part-time provides shift workers with flexibility

as it allows them to have time and energy to spend with their children and perform

domestic duties during their time off (Agosti, Andersson, Ejlertsson & Janlöv, 2015).

Age

A number of studies address the relationship between age and job satisfaction. There are

reports of a positive relationship (Chambers, 1999; Dawal & Taha, 2006; Haqur, 2004),

a negative relationship (White & Spector, 1987), a U-shaped relationship (Clark, Oswald

& Warr, 1996; Gazioglu & Tansel, 2006) or no relationship (Dhanapal, Alwie,

Subramaniam & Vashu, 2013; Rajput, Mahajan & Agarwal, 2017; Sarker et al. 2003; Tu,

Plaisent, Bernard & Maguiraga, 2005). The body of work thus illustrates conflicting

views on the relationship between job satisfaction and age.

Clark et al. (1996) suggested a U-shaped relationship between age and job satisfaction to

be particularly common for males and full-time employees. Researchers have asserted

that employees experience a high level of job satisfaction at the start of their career but


21

this starts to diminish by mid-life, and gradually ascends as ages increases. This is

hypothesised to link to employees adjusting to their work and life situation over time

(Gruneberg, 1979). This has been supported by Saleh and Otis (1964) where they

conducted a comparative study between two groups of male managers. The increase was

due to individuals adjusting to life as they age, and the decline was due to drop in physical

health as well as lack of self-actualisation and psychological growth.

The findings of Goswami et al. (2011) indicated that older employees were more satisfied

than younger shift workers. Furthermore, Parkes (2002) concluded that elder workers

tend to experience difficulties in adapting to shift work more than younger workers.

However, Wedderburn (1996) found that shift workers aged between 31-40 were more

satisfied than employees aged under 21 and over 50 years. Furthermore, Shields (2006)

noted that younger shift workers were more likely to experience job dissatisfaction when

she compared employees between 25 and 39 years old.

A qualitative study conducted in Australia by Shen and Dicker (2008) examined the

impacts of shift work on employees in the food industry. From the 24 employees

(managers and shift workers) interviewed, it was reported that elder employees faced

difficulties in adjusting to shift work, and they required more time to recover from their

shifts. However, the majority of employees felt that they were adjusting better to shift

work and were able to manage eating and sleeping patterns as they aged. The study found

that the retirement age of shift workers was lower than other types of jobs, this could be

due to the nature of the job, as it is mentally and physically demanding, that they have

been experiencing throughout the years of service. Therefore, organisations should

develop a Human Resources (HR) plan to maintain a sustainable workforce.


22

Educational level

According to Gruneberg (1979), a relationship between educational level and job

satisfaction is highly likely. Studies conclude that the relationship between the level of

education and job satisfaction can be either positive or negative. For example, Clark

(1997), Gruneberg (1979) and Vollmer and Kinney (1955) reported a negative

relationship between educational level and job satisfaction. They concluded that

employees with a high educational level tend to experience a lower level of job

satisfaction. This could be due to a highly-educated employee having higher expectations

of securing a good job which is interesting and with a high salary. Such employees are

more likely to have high aspirations for their lives and, if this expectation is not met, this

could lead to disappointment, thus resulting in job dissatisfaction (Binder & Coad, 2011;

Ross & Reskin, 1992)

The assumption of employees with higher level of education to secure a satisfying job

has been questioned by Quinn, Staines and McCullough (1974) where they stated that

several studies found this assumption to be oversimplified or wrong. In their study, they

concluded that there are no associations between the level of education and job

satisfaction. Idson (1990) and Kumar (2016) also reported no significant impact of

educational level on job satisfaction. However, Adeoye, Akoma and Binuyo (2014),

Rajput et al. (2017) and Sundar and Kumar (2012) found that educational level

contributes significantly to job satisfaction.

In terms of shift work, a higher level of education was found to have an inverse

relationship with job satisfaction among nurses as concluded by Al-Enezi, Chowdhury,

Shah and Al-Otabi (2009) and Larrabee, Janney et al. (2003). Unskilled employees can


23

earn a good income while working shifts and this contributes to their willingness to work

(Shen & Dicker, 2008).

Work experience

Work experience has an impact on the level of job satisfaction. New employees or fresh

graduates may be happy to get a new job, therefore; they are satisfied with what they get

(Gruneberg, 1979). A study conducted in 2015 in Hong Kong found that 76% of fresh

graduates are satisfied with their first job and a stronger result (79%) was found in

Malaysia (VT Comms, 2015; Adina, 2015). The more time an employee stays at work,

the more they draw a personal image of the place, and they hold sufficient experiences to

determine their satisfaction level with the job. Few researchers (Dawal & Taha, 2006; Tu

et al., 2005) have found a positive correlation between the level of job satisfaction and

job experience.

Furthermore, Nestor and Leary (2000) concluded that employees with more than 21 years

of experience are more satisfied than employees with one to five years. However,

Loscocco (1990) found that employees with the highest years of experience were the least

satisfied with their jobs. Yet, a quantitative study was conducted by Haq and Chandio

(2014) to analyse the level of job satisfaction for a sample of 500 employees working in

a service-oriented organisation using an Analysis of Variance test to conclude that there

was no significant relationship between the length of service and job satisfaction. Similar

conclusions were reached by Rajput et al. (2017) in their empirical study of job

satisfaction among masons.

In terms of working shifts, employees with more experience in shift work tend to know

how to deal with the difficulties of shift work. However, they experience more fatigue,

are less motivated, and more likely to have higher turnover rates. On the other hand,


24

employees with less experience tend to have higher absenteeism rate in comparison to

more experienced shift workers due to their limited experience in having control over

shift work effects. It was reported by managers, in structured interviews, that employees

left the organisation after working three to seven years due to the lifestyle associated with

shift work (Shen & Dicker, 2008).

Marital status

An employee's marital status is another variable that can impact on his or her job

satisfaction (Adeoye et al., 2014). Married employees could potentially be responsible for

providing financial support to their family; therefore, a difference in the level of

satisfaction could be found between married and single employees (Jayaraman, 2015).

Studies have found that married employees are more satisfied with their jobs than single

employees (Chambers, 1999; Dawal & Taha, 2006). This could be in result of the

excessive amount of responsibilities, such as child care and household responsibilities,

that married employees tend to have, making them value and adjust to their current job

(Azim, Haque & Chowdhury, 2013), thus restricting choice of alternatives and lowering

expectations (Lincoln & Kalleberg, 1985). However, other studies concluded that marital

status does not play a significant role in determining the level of satisfaction of employees

(Azim et al., 2013; Jayaraman, 2015).

Many individuals assume that shift work is hard on marriage. However, this is not always

the case. A study conducted by Xavier University researchers found that married

employees working shifts, including employees having children, had higher level of job

satisfaction in comparison to their un-married colleagues as they were able to adapt better

to shift work (Adams, 2004). However, Goswami et al. (2011) found that single shift

workers were more satisfied than married employees.


25

Bohle and Tilley (1998) conducted a longitudinal study, examining employee’s attitude

towards shift work and predictors of overall dissatisfaction among hospital nurses. They

found no relationship between marital status and job satisfaction among shift workers.

However, other researchers have identified that shift workers experience a low level of

marital satisfaction and a higher rate for divorce (Costa, 1996; Presser, 2000). This could

be due to the limited time couples spend together, especially if they have chidren, which

possibly increases the risk of separation and divorce (Presser, 2000). It could be that

couples with no children find it easy to cope with the stress associated with shift work,

thus not affecting their marital stability.

Gender

Gender is a key dimension discussed in the organisational behaviour literature

(Mahmood, Nudrat, Asdaque, Nawaz & Haider, 2011). Numerous studies (e.g. Jaradat et

al., 2017; Tuttle and Garr, 2012) have been conducted on the relationship between job

satisfaction and gender across different fields and work patterns. Various studies conclude

that male employees have higher job satisfaction levels than female employees (Bender

& Heywoo, 2006; Castillo & Cano, 2004). However, other studies identified that female

employees are more satisfied than male employees (Kaiser, 2007; Kim, 2005; Mahmood

et al., 2011; Zaman & Rahman, 2013). This could be a result of different gender job

expectations (Campbell, Converse & Rodgers, 1976). Still other studies have argued that

gender does not have any statistical significance with job satisfaction (Al-Ajmi, 2006).

Jaradat et al. (2017) report that there is no gender difference between shift work and job

satisfaction among nurses. A similar finding was supported by Goswami et al. (2011),

where they found no significant difference between gender and the level of job

satisfaction among shift workers. However, Beers (2000) and Presser (2003) found that


26

males tend to work more non-standard hours than females. In contrast, a 2012 study

conducted by Tuttle and Garr (2012) to investigate the effects of shift work on individuals

by using questionnaires and telephone interviews noted that shift work increased in

service sector jobs which are female-dominated. They found that males and females had

different views about shift work; generally, females do not prefer shift work due to their

family responsibilities and commitments. However, if their job requires non-regular

working hours, they tend to schedule their shifts to meet their domestic obligations.

Furthermore, a qualitative study examined the impacts of shift work on shift workers in

the food industry and concluded that males tend to handle shift work better than females;

however, one interviewee reported that 30-50 years old women are more productive in

comparison to their counterparts. Therefore, gender is not a moderating factor (Shen &

Dicker, 2008).

Family and number of dependent children

It is widely known that work and family are interrelated domains. Family and household

duties can be energy consuming and often leads to family-work conflict (FWC) and/or

work-family conflict (WFC). WFC are conflict that employees experience between their

work and family responsibilities (Greenhaus & Beutell, 1985). In contrast, FWC arises

when family responsibilities interfere with work. WFC causes employees to develop

negative attitudes towards their job, in turn affecting their job satisfaction which impacts

on overall organisational performance and success (Gozukara & Çolakoğlu, 2015).

Several research studies identified that WFC has a negative impact on employee job

satisfaction (Glaveli, Karassavidou & Zafiropoulos, 2013; Gozukara & Çolakoğlu, 2015).

However, the effect was able to be minimized by managerial support through appreciation

and attention of employees' contribution, demonstrating an understanding towards


27

employee's family responsibilities, as well as providing workshops and regular

psychological support (Bakker & Demerouti, 2007; Gozukara & Çolakoğlu, 2015; Karimi

& Nouri, 2009). Showing support and understanding towards family conflict could

provide the organisation with a favourable reputation as a family-oriented employer

which can improve potential recruitment and employee retention.

In contrast, several studies have argued that family responsibilities have no effect on job

satisfaction (Baral, 2016; Gao & Smyth, 2010; Jayaraman, 2015; Nielsen & Smyth,

2008). However, Dartey-Baah (2015) stated that, when it is difficult to balance between

family and work, FWC and WFC causes negative impacts on job satisfaction. Luo (2016)

and Tuttle and Garr (2012) outlined that family responsibilities have a stronger effect on

women's job satisfaction compared to that of men. This could be due to married women

preferring to work in a less demanding job so, enabling them to manage professional and

personal lives simultaneously (Beauregard, 2007).

Quinn et al. (1974) mentioned a national study where it was discovered that women who

have one or more children and they are less than six years old experience lower job

satisfaction than women who had no children under this age. Crosby (1982) found that

single employees and married employees without children are less satisfied with their

jobs than those married with children. This could be a result of family responsibilities and

commitments, which in turn shifts attention from work towards family. However, Hanson

and Sloane (1992), Lorence (1987) and Moen and Smith (1986) found that young children

have no impact on job satisfaction.

A few studies have documented that females experience problems in balancing shift

work and family life more than males (Mills, 2014). However, Gerson (2009) suggested

that men are also experiencing such an issue. Female employees with young children have


28

reported tiredness and interrupted sleep after completing night shifts (Costa, 1996). Shen

and Dicker (2008) found that married employees who work shifts and have children are

more likely to experience family conflict because of their inability to participate in family

duties.

According to Presser (2000), men married for less than five years, with children and

working fixed night shifts were six times more likely to divorce than those who work day

shifts. In contrast, women who were married for more than five years with children were

three times more likely.

The inconsistent findings highlight the need for further research regarding the personal

demographics of shift workers in order to achieve clarity. This study will identify the

impact of the demographic variables of nature of work, position, employment type, age,

education level, work experience, marital status, gender and number of children so as to

determine an employee’s level of job satisfaction.

2.5.2 Risk factors

Shift work has been identified as a workplace hazard due to the social and work related

risks (Rosa & Colligan, 1997). In recent decades, numerous studies (e.g. Faragher et al.,

2005; Castro, Carvalhais & Teles, 2015; Silva, Prata, Ferreira & Veloso, 2014) have

documented the impact of shift work on individuals and organisations. As illustrated in

Figure 2, shift workers experience issues with stress, health, poor sleep, fatigue, family

life, and isolation. For this review, we acknowledge the inter-relationship between the

aforementioned variables. However, due to the limited extent of research available on

shift work within the aviation sector, the review will examine different sectoral contexts,

which may share some of the features of the working environments of the aviation sector.


29

Figure 2. Risk Factors

Stress

Stress is a psychological factor which is a result of irregular sleeping hours, WFC/FWC,

workload, performance pressure and relationship with others (Monk & Folkard, 1992).

According to existing literature, stress is identified to have a negative impact on job

satisfaction (Fairbrother & Warn, 2003; Kula, 2017; Zeytinoglu, Denton et al., 2007).

Shields (2006) have determined a strong association between the level of job stress and

job satisfaction among shift workers. Stress causes threat to an individual’s physical and

psychological well-being, as well as to his or her quality of life, job performance and job

satisfaction (Firth-Cozens, 1993; Mansoor, Fida, Nasir & Ahmad, 2011). Several studies

(Gruneberg, 1979; Spector, 2012) have revealed that stress leads to physical illness such

as heart diseases, headache and peptic ulcer. Furthermore, Costa (2000) indicated that

activities performed by Air Traffic Controllers lead to stress-related symptoms such as

fatigue, headache, heartburn, chest pain, indigestion, hypertension, coronary heart

disease, diabetes, peptic ulcer and psychoneurotic disorders. Conway, Campanini et al.





Risk Factors Stress

Health issues Difficulty falling asleep

Fatigue Family conflict

Isolation



30

(2008) established that stress associated with work has most relevance to an individual’s

lack of health.

Jamal and Baba (1992) argued that the level of stress is related to the nature of work.

Their quantitative study of 1,148 nurses at eight hospitals in Canada found that those

working fixed shifts experienced less stress in comparison to those performing rotating

shifts. Moreover, employees working in non-intensive care departments experience less

stress in comparison to employees working in the intensive care department.

In addition, lack of supervisor and co-workers support, customers and family conflict can

also cause stress. Lack of support from supervisor has an influence on the level of stress

in the workplace. This is evident in the work of Sargent and Terry (2000) which

discovered that a high level of support given by a supervisor decreases employees’ stress

level, in turn increasing employee job satisfaction. Moreover, co-worker support (Sargent

& Terry, 2000) and attitude (Weiss & Shaw, 1979) play a role in minimizing stress while

customer incivility and aggression also have a negative impact on stress and overall

employee job satisfaction (Kim, Ro, Hutchinson & Kwun, 2014). Shift workers were also

found to experience stress due to lack of social and family time (Baker, Fletcher, &

Dawson, 2000).

A study conducted by Iqbal and Waseem (2012) examined the impact of job stress on job

satisfaction for Air Traffic Controller of the Pakistan Civil Aviation Authority. They

reported a negative relationship between job stress and job satisfaction, where employees

who had high stress level experienced low levels of job satisfaction in contrast to those

with low stress experiencing higher levels of job satisfaction. In addition, Newbury-Birch

and Kamali (2001) measured the relationship between stress and job satisfaction in a

sample of 109 group pre-registration house officers in England. Results illustrate that


31

women experience more psychological stress than men and the mean job satisfaction for

men to be higher than women. They concluded that a significant negative correlation

exists between stress and job satisfaction, determined by using Spearman's rank

correlation test.

It has been suggested by Rosa and Colligan (1997) to perform regular exercise in order

to reduce stress and illnesses (e.g. 20 minutes of jogging, swimming, or any aerobic

exercise). However, exercising three hours before sleep is not advised as it tends to

activate the body, making it difficult to sleep. They also recommend avoiding heavy

meals full of fat and sugar especially at night.

Health issues

Shift work was found to have a negative impact on employee health and well-being

(Jaradat et al., 2017). It was discovered that shift workers experience sleep disruption and

poor eating habits in the short-term, whereas in the long-term, they are at risk of developing

chronic fatigue, cardiovascular disease, gastrointestinal disease (due to poor eating habits),

obesity, cancer, peptic ulcers, metabolic syndrome and abnormal blood glucose levels

(Åkerstedt, 2003; Hartenbaum & Zee, 2011; Parent, El-Zein, Rousseau, Pintos &

Siemiatycki, 2012; Phillips & Houghton, 2007). As per Haalebos (1998), Folkard stated

that employees who have been working shifts for two to three decades are twice or thrice

as more likely to experience heart diseases than regular day employees.

Megdal, Kroenke, Laden, Pukkala and Schernhammer (2005) conducted a systematic

review and meta-analysis of 13 observational studies to assess the link between breast

cancer risk in women working night shifts, majority being cabin crews. A positive

relationship was identified; however, the reason was unclear but one could assume that it

could be due to working night shifts which reduces the production of a hormone that is


32

considered to have a cancer-preventing effect. Furthermore, in 2008, The International

Agency for Research on Cancer (IARC) found that shift work that involves circadian

disruption could possibly increase the risk of cancer, especially breast cancer in women,

thus validating the previous assumption (Aspen Publishers Inc., 2008). In addition, a few

studies have reported a possible risk of prostate cancer among male employees who work

night shifts (Dapang, Yu, Bai, Zheng & Xie, 2015; Lin, Tu, Chen & Guo, 2016; Parent et

al., 2012). However, Åkerstedt, Narusyte, Svedberg, Kecklund & Alexanderson (2017)

found no association between working a night shift and prostate cancer. Furthermore,

Bøggild and Knutsson (2000) concluded that night shift workers have a 40% higher risk

of cardiovascular diseases; this was determined by shift workers having a high cholesterol

level, triglycerides, obesity and Body Mass Index (BMI).

Organisations need to acknowledge the significance of health issues associated with shift

work and take necessary action in order to decrease its deleterious impacts on employee

health. This could be by providing shift workers with free or subsidised medical insurance,

enabling an employee to have regular access to medical check-ups covered by the

insurance plan inclusive of any costs. In addition, access to health care and counselling

can provide support for employees to improve physical and mental health (Rosa &

Colligan, 1997).

Providing employees with access to facilities which offer healthy food options would also

contribute in supporting an effective employee well-being campaign. Gatwick Airport in

London, conducted a 12-week intervention programme called "Passport to Health" where

35 shift workers were offered advice regarding their diet and physical activity, and offered

low calorie meals in staff cafeterias. The findings for the programme identified a decrease

in weight, body mass index, waist circumference, glucose level and cholesterol level


33

among the 35 participants (Ruxton & Ursell, 2015). Based on these findings, it can be

suggested that providing workers with a healthier meal option would potentially result in

an increase in employee health and well-being.

Poor sleep quality

It is evident that night shifts significantly affect an employee’s health and well-being.

Therefore, it should be noted that time-oriented body functions can also lead to health

issues due to not having quality and sufficient sleep which the body requires (Finn, 1981).

Disturbed sleep is the most common consequence of shift work as irregular work hours

result in receiving insufficient sleep (Åkerstedt, 2003; Costa, 1997; Monk & Folkard,

1992). This has been identified as leading to the consumption of alcohol through falling

asleep or excessive smoking to staying awake at night (Martinussen & Hunter, 2010).

Tired employees are at risk to themselves as well as to their organisation and their

customers (Hartenbaum & Zee, 2011). The human body requires a certain number of hours

of sleep per night. Over a period of inadequate and poor quality sleep, the body experiences

sleep debt which diminishes alertness, logical reasoning, and decision-making abilities

thus resulting in fatigue (U.S. Coast Guard, 2001; Vila, Morrison & Kenney, 2002).

The relationship between shift work and poor sleep, poor work ability and job

dissatisfaction was evident in the work of Conway et al. (2008) who investigated Italian

healthcare workers. They concluded that health issues have a moderate negative

correlation with job satisfaction. Furthermore, Karagozoglu and Bingöl (2008) found a

negative correlation between sleep quality and job satisfaction among nurses. Shift work

limits the time an employee spends with their friends and family. Therefore, they tend to

neglect sleep which increases employee job dissatisfaction, absenteeism and turnover

(Baker et al., 2000; Shen & Dicker, 2008). In order to minimise these variables,


34

organisations could periodically identify employees who are experiencing sleep disorders

through health checks and transfer them to day work and monitor their progress.

Fatigue

A fatigued employee is identified as being short tempered, less motivated and alert, and

possessing poor communication skills (Sadeghniiat-Haghighi & Yazdi, 2015). Such an

employee is at a risk of experiencing fatigue regardless of the training, knowledge or skills

they possess. Fatigue can affect an individual’s performance, and increase the risk of

human error which could potentially have a significant impact on personal, organisational

and public safety (Sadeghniiat-Haghighi & Yazdi, 2015). As mentioned previously,

fatigue can be expressed in the form of lack of sleep, energy and motivation (Åhsberg,

Kecklundb, Åkerstedt & Gamberalea, 2000). From an aviation perspective, fatigue is a

“condition characterized by increased physical and/or mental discomfort with reduced

capacity for work, reduced efficiency in task accomplishment and motivation or capacity

to respond to stimulation, typically accompanied by weariness and tiredness” (Ferguson

& Nelson, 2012, p. 161). Therefore, aviation organisations have adopted an active fatigue

risk management programme in order to avoid fatigue and to monitor and to track fatigue-

related issues among staff (Ferguson & Nelson, 2012). The programme includes training

and workshops on how to manage fatigue and sleep. Fatigue within the aviation sector is

often a direct result of shift work which is due to lack of quality and adequate sleep

(Ferguson & Nelson, 2012).

Shift workers, specifically those on night shifts, experience the highest level of fatigue

due to poor sleep quality, thus resulting in a high rate of absenteeism (Åhsberg et al., 2008).

This is further supported by Folkard and Tucker (2003) who confirm that safety and

productivity of employees lessen at night. A study conducted in New Zealand to determine


35

fatigue among truck drivers and the degree of its effect on their driving performance

identified a significant level of fatigue among the drivers, with 24% of the participants

failing a standard computer test (Charlton & Baas, 2001).

Shah and Nina (2009) conducted a study to measure the relationship between fatigue and

job satisfaction among doctors working at different hospitals in an Indian context using

structured questionnaire with a sample of 110 participants. They found a significant

negative correlation between fatigue and job satisfaction where, when fatigue increases,

job satisfaction decreases and vice versa. Similar results were found by Choi and Kim

(2013) in their quantitative discriptive study to identify the relationship between fatigue

and job satisfaction among 162 clinical nurses at two small and medium-sized hospitals in

two different cities, however, the researcher did not reveal where the study had been

conducted.

The New Zealand Ministry of Business, Innovation and Employment (a super-ministry

which replaced the Department of Labour) recommends that employees should not work

for more than three or four consecutive night shifts as working for more than three to four

nights consecutively can cause shift workers a loss of an hour to an hour and a half of sleep

a day, thus causing 4.5 to six hours of sleep debt. Furthermore, it is not recommended for

employees to work for more than six hours for three successive days between 12:00 and

06:00, following an 18-hour period over the three days, employees should receive two

complete days off shift to recover and achieve adequate sleep (Department of Labour,

2007). Therefore, the number of successive night shifts, length of night shifts and rostered

days off between shifts should be considered when creating rosters in order to reduce the

overall impact of shift work on job performance and safety.


36

Family conflict and social isolation

A vast amount of research has documented the negative impacts of shift work on an

employee's family and social life (Finn, 1981) as irregular working hours and a

requirement to work during holidays and weekends, and feeling excluded from the people

around them. Shift work leads to FWC, WFC, low level of family adjustments (Stains &

Pleck, 1984; Costa, 1997), less time for leisure and community/social activities (Baker et

al., 2000; Mills, 2014), low spouse satisfaction (Smith, Folkard et al., 1998) and lower

attendance at childcare and school events attendance (Costa,1996; Shen & Diccker, 2008).

However, Demerouti, Geurts, Bakker and Euwema (2004) argued that shift work could be

seen as an opportunity which allows employees to spend more time with their family and

friends if adequate days off were provided. Demerouti et al. (2004) have suggested that

fixed non-day shifts including weekends be avoided in order to reduce WFC. Experiencing

a disrupted social or domestic life may result in employees feeling isolated, thus affecting

their health and performance (Health and Safety Excutive, 2006).

The impacts of shift work on family and social life differ between those who are married

and those that are single. Married employees feel that having an understanding partner is

very important to reduce family conflict and those with children are more likely to be

affected as shift work restricts their availability, thus resulting in being absent from co-

curricular school activities (Shen & Dicker, 2008). However, Kaliterna, Prizmic and

Zganec (2004) found no difference in the overall happiness, life satisfaction or total quality

of life of shift workers in comparison with non-shift workers despite reports of shift

workers being more physically tired.

In most workplaces, employees do not experience isolation due to their interaction with

co-workers and supervisors and, in specific jobs, interaction with customers (Janseen &


37

Yperen, 2004). However, employees working rotating shifts may work with different

employees depending on the schedule, thus preventing them from developing strong

relationships and leading to a decrease in the levels of co-workers support (Mills, 2014).

Furthermore, specific roles create isolation, for example; night shift work, where

employees in specific departments have minimum interaction with co-workers and

customers due to the nature of the job. Role isolation creates stress, thus affecting the level

of job satisfaction as concluded by Dhawan (2015) in his empirical analysis on 480 bank

employees in India. Furthermore, isolation could affect an employee’s morale, thus

affecting his or her job satisfaction and job performance. Therefore, the organisation

should plan events that could help to minimise the feeling of isolation among employees,

for example, by sponsoring sports teams and organising social programmes such as get-

togethers and sports activities.

The majority of the studies reviewed here focused on the impact of shift work on the risk

factors. However, only a few have explored the impact of the risk factors on job

satisfaction among shift workers. Therefore, in this study, the discussed risk factors are

important variables to measure in terms of the extent of their relationship with job

satisfaction in the aviation context.

2.6 Research Questions

Based on this review, it emerges that numerous studies have been conducted in relation

to job satisfaction in various sectors. However, there still remains a need to further

investigate this topic, especially in the aviation sector, due to insufficient research (see

section 1.4). The factors that influence job satisfaction levels for shift and other workers

need to be conjointly measured because available research is equivocal and has not made

any definite conclusions about job satisfaction determinants. This is significant given


38

employee job satisfaction levels’ noted relationship to individual and organisational

outcomes such as employee productivity, withdrawal behaviours and organisational

performance. In relation to shift workers in the aviation sector, this research will address

the following research questions:

1. Does job satisfaction vary according to personal demographic variables?


risk factors?; and


An initial conceptual framework of this study is illustrated in Figure 3. It describes the

possible relationships between personal demographic variables, risk factors and job

satisfaction.

Figure 3: Initial Conceptual Framework





Risk Factors Stress



Isolation



39

2.7 Summary

This chapter has provided an overview of the studies on job satisfaction, including

definitions and seminal theories on job satisfaction, job satisfaction and shift work

including reasons for working shifts, and factors affecting job satisfaction (personal

demographics and risk factors). It emerged that there is equivocal research in relation to

the personal demographic variables and job satisfaction and not enough studies have

explored the extent of the relationship among the risk factors and job satisfaction among

aviation shift workers. Thus, further research is required to fill the identified research

gaps. These gaps informed the development of several key research questions for this

study. In response to the research questions, the next chapter will detail the

methodological approach of this empirical inquiry.


40

Chapter 3 – Research Method

3.1 Introduction

The objective of this chapter is to discuss the research methodology and techniques that

have been used in this study. In order to address the research questions outlined in Chapter

2, Section 3.2 restates the study’s purpose, along with its main research questions. Section

3.3 introduces and justifies the selection of particular research paradigms and the adoption

of a case study approach. It also states the reasons for choosing a single organisational

case study. Section 3.4 elaborates on the data collection approach, along with a

justification for using the main research tool (a survey). Section 3.5 presents the access

procedure, selection process and research context. Following this, ethical considerations

are discussed in Section 3.6. Section 3.7 elaborates on the target participants. The pilot

study which improved the survey design and the data collection process is discussed and

elaborated in Section 3.8, along with content of the survey and the instruments used in

this study. Section 3.9 explains the data collection procedure. Section 3.10 describes the

survey diagnostics. Section 3.11 explains the quantitative and qualitative data analysis

approaches. Finally, the chapter is summarised in Section 3.12.

3.2 Research Purpose

Employee job satisfaction is a key component of the work environment and organisational

climate. There are personal demographic variables and many risk factors which can

influence an employee’s job satisfaction. The purpose of this study is to explore the

impact of these demographic variables (department, position, employment type, age,

educational level, work experience, marital status and number of dependent children) and

risk factors (stress, health issues, difficulty falling asleep, fatigue, family conflict and


41

isolation) on job satisfaction of shift workers in the aviation sector in New Zealand.

Specifically, this research will address the following research questions:

1. Does job satisfaction vary according to personal demographic variables?

2. What is the nature and extent of the relationship between job satisfaction and

the risk factors?; and


3.3 Research Approach Overview

3.3.1 Research paradigms

Positivism is occasionally referred to as scientific research (Lehmann, 2010). It is “based

on the rationalistic, empiricist philosophy that originated with Aristotle, Francis Bacon,

John Locke, Auguste Comte and Immanuel Kant” (Mertens, 2010, p. 10). The role of the

researcher is limited to the collection and interpretation of the data through an objective

approach where there is a minimal interaction between the researcher and the participants.

Positivist researchers follow a quantitative approach, which is a “formal, objective,

systematic process in which numerical data are utilised to obtain information about the

world” (Burns & Grove, 2005, p. 23). It is often called critical or postmodern research

(Creswell, 2003). It is inclined to be deductive and “develops a theoretical or conceptual

framework, which you subsequently test using data” (Saunders, Philip & Thornhill, 2012,

p. 61).

This study primarily relied on positivist research approach and quantitative techniques as

they are more suitable for fulfilling the purpose of answering the research questions and

further builds on quantitative tradition (see section 2.3). Indeed, most studies which


42

measure employee job satisfaction have used a quantitative approach (Adeoye et al.,

2014; Burch, et al., 2009; Rajput et al., 2017; Shafiq & Ramzan, 2013; Thompson &

Phua, 2012).

This study also implemented elements of an interpretivist research paradigm.

Interpretivists believe that reality is multiple and relative (Hudson & Ozanne, 1988). The

researcher tends to rely on the "participants' views of the situation being studied"

(Creswell, 2003, p. 8). Interpretivist researchers follow a qualitative approach, which

allows the researcher to study perceptions, ideas and opinions. Therefore, this study has

employed a qualitative approach in order to provide detailed feedback and yield richer

and more revealing responses from participants (Grandcolas, Rettie & Marusenko, 2003).

Beyond the approach adopted in many job satisfaction studies, the use of qualitative data

in this study is designed to supplement and enrich, as well as corroborate and refute, the

quantitative analysis.

3.3.2 Case study

The case study approach is an empirical inquiry that is often used when a real-world

contemporary phenomenon is comprehensively investigated (Yin, 2014). The case study

design involves an intensive analysis of a single or multiple cases, the term case is referred

to a location or an organisation. Case studies have been used by several researchers in the

field of management research (Mariotto, Zanni & De Moraes, 2014). Nevertheless, this

method arises questions regarding the external validity of the research as one or multiple

cases cannot represent an organisation or a group of organisations (Bryman & Bell, 2015).

However, Dyer and Wilkins (1991) argue that a single-case study is better than a multiple-

case study as it provides detailed observations and existing theoretical relationships are

questioned and new ones are explored, which enables an extensive examination and a


43

deep understanding of the situation. Thus, a single-case study approach was adopted for

this research. Furthermore, due to the high cost and time consuming multiple case studies

need for implementation, a single case study was implemented.

According to Yin (2014), there are several rationales for a single-case study. In terms of

this study, firstly, research on shift work within the aviation sector in New Zealand

appears to be relatively scarce. Therefore, examining the case organisation represents a

significant contribution to the knowledge and extending existing theories. It also helps to

re-enforce future investigations in the field. Secondly, the case organisation is unique as

it is classified as the world’s best organisation and considered a key player in driving New

Zealand economy which is worth analysing and investigating. Thirdly, shift work is a

common case were conditions and settings are captured in daily situations where they

require around the clock service. Finally, the phenomenon explored in this study is not

previously examined by researchers in a social science inquiry. Therefore, a single

organisational case inquiry provides the ‘best fit’ for this exploratory inquiry.

3.4 Data Collection Approach

Within the case inquiry, a cross-sectional survey tool has been used for several reasons.

Firstly, it gives researchers the ability to compare many different variables at a single

point in time and the ability to generalise the results with the least social desirability

effects (Bryman & Bell, 2015). Secondly, due to the participants not being deliberately

exposed to the researcher (only minimal interaction with researcher, that is, through

emails), there are minimal ethical difficulties (Mann, 2003). Lastly, it elicits data that

would enable a comprehensive and robust responses. Due to time constraints, it was not

feasible to undertake a longitudinal study were researchers are allowed to measure the


44

change in variables over time, thus enabling the prediction of future outcomes (Bryman

& Bell, 2015).

There are several methods for data collection techniques which can be considered for this

study. Attitudes towards work can be assessed through both interviews and surveys. A

survey was considered to be the most appropriate instrument here due to its highly

structured techniques, which are also useful for studies of the beliefs, values and attitudes

regarding a particular situation (Bowditch & Buono, 2004). Surveys, and in particular

online surveys, are relatively inexpensive, have higher answered questions with less

missing data in comparison with other types of surveys (e.g. supervised and postal

surveys), and are most frequently used method in collecting substantial amounts of data

(Bryman & Bell, 2015). Participants tend to find it easier to answer surveys online at their

own convenient time instead of using paper and pen and also they tend to provide more

detailed replies to open ended questions, if any are available (Bryman & Bell, 2015).

Furthermore, online surveys provide the advantage of using an attractive format and

design, and reduces the possibility of skipping questions as web-based surveys can be

programmed where only one question is visible, thus removing the likelihood of missing

data (Gelder, Bretveld & Roeleveld, 2010). In addition, it enables the automatic

processing system to easily and quickly collect data for analysis. However, it requires a

great level of trust on the part of the respondents. If any suspect that they will be

identified, they may either refuse to participate or they may not respond truly (Bowditch

& Buono, 2004).

Online surveys are also associated with non-response bias. However, to overcome this

problem, it is advised to contact the prospective participants prior to sending them the

survey, send them a reminder email at least once and provide progress indicator bar in


45

order to reduce the number of participants who may quit the survey while they are half

way though completion (Bryman & Bell, 2015). The risk of multiple replies is common

in online surveys, where participants can undertake the survey more than once, thus

leading to inaccurate results. However, few online survey platforms have control over

this issue (see Section 3.8.2). For the above-mentioned reasons, despite the limitations

associated with them, an online survey was used in this study.

3.5 Access and Selection

3.5.1 Access

A large New Zealand airport was contacted in early November 2016 to gain approval to

conduct the study. The People and Capability Advisor, at the time, granted permission to

conduct the study upon approval of the head of department. In October 2016, I was

notified by the airport company that the Acting General Manager for Aeronautical

Operations had declined the request due to increase in traffic expected over the summer

period and also structure change within the operations department. However, they

suggested that they may be able to provide support and participate in the research if it was

delayed till early March 2017. As this would have resulted in a significant stretch of my

set time-frame, an alternative organisation within the sector with similar working

conditions was sought.

After examining several workplaces, I decided on, and sought and obtained permission

to conduct the study within the case organisation from the organisation’s Strategic

Wellbeing Manager (Appendix A). A wellbeing programme consultant was assigned to

support and facilitate the survey. The consultant provided e-mail addresses of those

employees working shifts.


46

3.5.2 Research context

This study was conducted in an aviation organisation in New Zealand between December

2016 and February 2017. The organisation belongs to the aviation sector which is one of

the most important sectors, being operational 24 hours a day, 365 days a year. The

operation is reliant on individuals who work in shifts with various patterns, durations and

conditions throughout its operational period.

The case organisation was selected due to it being one of the best organisations for the

fourth consecutive year (2013-2016) in global rankings within its sector (New Zealand

Media and Entertainment, 2016). The organisation is a key player in driving the local

economy and supporting travel, tourism and trade (Case Organisation, 2015). It supports

over 11,200 jobs across the country and is a significant contributor to trade through its

cargo and export service. In addition, it brings 40% of visitors to New Zealand in order

to aid the nation's tourism industry (Case Organisation, 2015). In addition, it provides

services to several airports in New Zealand, including ramp and cleaning, baggage, airport

and passengers and operations performance.

3.6 Ethical Considerations

As with all research, there were ethical considerations for this inquiry and Massey

University procedures were observed. Prior to initiating the research fieldwork, ethical

aspects of the study were discussed with supervisors, researchers and academic peers. An

ethics check-list questionnaire was answered, and a summary of the project and ethical

issues considered in the study were provided. Finally, a low risk notification application

was submitted to the Massey University Human Ethics Committee (MUHEC), with

approval received on 11 June 2016 (Appendix B).


47

With regard to the study participants, their privacy was guaranteed by non-disclosure of

their identities and aggregation of data results. In addition, all participants were provided

with all information in the email (Appendix C) relevant to their decision to participate.

Participant consent was also obtained and stored securely electronically. Individuals had

the right to withdraw from the study at any time.

With respect to data analysis, only the researcher and supervisors were privy to the data

and analytic output. All data were processed via statistical software (SPSS) on a

password-accessed computer. Regarding data management, all hard copies of results and

analytic output were maintained in a lockable filing cabinet and on a password-accessed

computer in the researcher’s office. The researcher conformed with the MUHEC

requirements around eventual data disposal (by deleting relevant files and shredding hard

copy material).

3.7 Target Population

In order to avoid sampling error, the target population was the entire population who work

shift in the case organisation (Jacobs, 2013). According to the well-being consultant

(personal communication, November 30, 2016), 922 employees work within four

different departments (see Table 2). Given the purpose of the study, there were no

constraints on the type of professions held by informants.


48

Table 2

Number of Employees in Each Department

Two employment statuses (full-time and part-time) were included in the study as these

are most dominant in the organisation. Full-time employees spend around 24% of their

time at work (2,080 hours per year). Part-time employees spend approximately 18% of

all their time at work (1,560 hours per year) (personal communication, November 30,

2016). Both types of employment follow shift work patterns of the organisation. The

organisation follows an eight-hour shift pattern (with three early shifts, three late shifts

and three days off) or a 10 hour shift arrangement (with four days on and four days off

work) (personal communication, November 30, 2016).

The ramp team guides the planes into gates and push them back again. They are also

responsible of loading and unloading of baggage and washing planes windows under all

weather conditions. The cleaning team is in charge of keeping all aircrafts clean and

presentable. This job requires employees to be physically fit, patient, and able to work

under pressure (Case Organisation, 2017). The baggage services team is in charge of

handling bags securely and making them ready for travellers on time. This job requires

employees to be physically fit and able to work under pressure regardless of the weather

condition (Case Organisation, 2017).

Department Number of employees

Ramp and Cleaning Services 339

Baggage Services 180

Airport and Passenger Services 386

Operations Performance 17

Total 922


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The airport and the passenger services team is the face of the organisation, where they

provide help and guidance to the travellers in a very busy working environment. They

strive to provide outstanding customer service to increase customer’s trust, loyalty and

satisfaction thus increasing brand awareness. The job requires employees to deal with

travellers with special needs, diverse cultures, attitudes and different scenarios. Therefore,

this role requires employees to be passionate, energetic, flexible, easy to adapt to different

scenarios and have a great personality (Case Organisation, 2017).

The operations performance team is responsible for ensuring that all planes are ready to

operate. This department needs to follow a schedule; therefore, employees need to be

highly committed and consistent (Case Organisation, 2017).

3.8 The Survey

3.8.1 Pilot study

As described by Prescott and Soeker (1999), a pilot study is a smaller version of a larger

scale study or a trial run of methods and/or measures. It enables researchers to identify

flaws, create data collection and analysis plans and attain feedback from participants

(Beebe, 2007). In social science research, a pilot study is described in two different ways

of implementing into a study: i) as a feasibility study to conduct a small trial of the larger

study or ii) as a method of assessing the instrument that will be used in the primary

research (Van Teijlingen & Hundley, 2001). In this research, a pilot study was used to

assess the instrument’s face and content validity.

The survey for the pilot study was open for two weeks from 29 September to 6 October

2016. All responses were recorded by Qualtrics (online survey software). In this study,

content and face validity were established by five potential participants whom were


50

academics, researchers and sector specialist (former shift worker) with respect to the

survey questions utility to the shift workers in the aviation sector as a whole, and specific

relevance for the case organisation. They were asked to examine the instrument for

clarity, content, length and format.

Participants suggested clarifying questions to specify what exact information is required

from the questions, to provide contact details of researcher if any queries arose, indicating

whether the employees have been contacted by their management about the survey, and

finally, the age question is sensitive. Therefore, it was advised to provide age groupings.

In line with Lancaster, Dodd and Williamson’s (2004) view, participants in the pilot study

were not included in the main study as they were not affiliated with an aviation

organisation nor were they shift workers.

3.8.2 Survey content

The first section of the survey consisted of questions concerned with nine personal

demographic variables to deepen understanding of their impact on job satisfaction among

shift workers. These variables include: department, employment type, position, age, level

of education, work experience, marital status, gender and number of dependent children

(Appendix D). The second and third sections of the survey consisted of a mix of open and

closed-ended questions. Two types of closed questions were chosen for this research:

Likert and ranking scale. Many of the close-ended questions, with a five-point Likert

scale, were used to explore job satisfaction, reasons and drawbacks of working shifts.

Closed-ended questions were used as they are easy and quick to complete by ticking

answers, enhance the comparability of answers and provides the ability to clarify

questions by providing choices. However, answers are limited and fixed where

participants cannot provide interesting replies (Bryman & Bell, 2015). Therefore, a


51

limited number open-ended questions were also included in the survey to allow the

participants to freely express their opinions and provide further clarification of their

answers (Zikmund, 2003). It also allows the researcher to find unexpected responses

regarding the topic.

Several factors were taken into consideration while designing the survey including

rephrasing few statements in the instruments to adapt to the New Zealand cultural

definitions, the inclusion of instructions about how to respond to each question, providing

progress indicator and presenting each question separately in order to reduce non-

response bias (Bryman & Bell, 2015). Furthermore, participants were prevented from

taking the survey multiple times via the Prevent Ballot Box Stuffing option, which places

a cookie on the respondents’ browser when they submit a response. In order to overcome

the problem of non-response bias, survey questions were carefully ordered from least

sensitive to most sensitive and questions were precisely worded (Grandcolas, Rettie &

Marusenko, 2003).

3.8.3 Survey instrument

In this study, job satisfaction was measured using a version of the Index of Job

Satisfaction (IJS) developed by Brayfield and Rothe (1951). This instrument measures

overall job satisfaction in a wide variety of jobs by combining the Thurstone and Likert

scaling methods (Khaleque & Rahman, 1987; Thompson & Phua, 2012). According to

Thompson and Phua (2012), IJS has been used in over 550 research studies such as

Castillo and Cano (2004), Hoekstra (2014), Khaleque and Rahman (1987) and Taghipour

and Dezfuli (2013). The IJS scale is an effective and common scale due to its extensive

use in research, and due to its reliability, is the most appropriate measure for this research.


52

According to Brayfield and Rothe (1951), the IJS consists of 18 items with a reliability

coefficient of .77 which was corrected by the Spearman-Brown formula to .87. Each of

the items has a five-point Likert scale from strongly agree to strongly disagree. The scale

contains 9 positive and 9 negative statements. The scoring of each statement varied. Items

1, 2, 5, 7, 9, 12, 13, 15 and 17 were scored as: Strongly agree=5, Agree=4, neither agree

nor disagree=3, Disagree=2 and strongly disagree=1. The scores of the rest of the

questions were reversed, and recorded in Statistical Package for the Social

Sciences (SPSS) Version 24 as (1=4, 2=3, 3=2, 4=1). The total score was calculated by

adding all the numbers from each answer’s rating. The range of a possible total score was

between 18 and 90, with 54 being the neutral point. Higher scores represented job

satisfaction and lower scores job dissatisfaction.

During implementation, a system glitch resulted in the survey being distributed with one

of the 18 items not being visible to the respondents. After consulting several academics

and researchers, two preferred options emerged. The first was to find a shorter version of

the planned index and calculate its reliability and, if valid, proceed with it. The second

was to calculate the reliability with the missing item; if the reliability score was greater

or equal to .7, then it is considered satisfactory acceptable (Roni, 2014), and the analysis

could proceed. After finding shorter versions and assessing the reliabilities for each using

Cronbach's alpha, adoption of the full version of the index with a missing item (item 1)

was considered the better approach (refer to Table 3). The scale thus contained 8 positive

and 9 negative statements. The total score was calculated by adding all the numbers from

each answer’s rating. The range of a possible total score for the IJS was between 17 and

85, with 51 being the neutral point.


53

Table 3

Reliability of Job satisfaction

The survey also included three questions adapted from the Standard Shift Work Index

(SSI) created by the Shift Work Research Team at Swansea University (Barton et al.,

1995). The first question determined the motive of working shifts by ranking the nine

statements provided with 1 being most important and 9 being the least important. The

second question determined how often the participants experience the drawbacks of

working shifts. Each drawback has a five-point Likert scale from always (5) to never (1).

The third question determined whether the overall benefits of working shifts outweigh

the drawbacks with a five-point Likert scale from strongly agree (5) to strongly disagree

(1).

At the end of the survey, participants were asked if they would like to receive a free copy

of the aggregated results.

3.9 Data Collection Procedure

The case organisation manager emailed all employees who work shifts to inform them

about the company's participation in this study (Appendix E). Survey invitation letters

were then distributed on 10 December 2016 via an online survey platform (Qualtrics

Software); the email outlined the purpose and rationale for the study and included a link

to the online survey (Appendix C). After clicking on the link, participants were directed

to a webpage displaying the survey cover page (informed consent form). The cover page

(informed consent form) described the voluntary method of participation, assured strict

Cronbach's Alpha N of Items

.824 17


54

privacy and provided the option for withdrawal at any time. It also provided the estimated

duration of the survey (five to 10 minutes). The purpose of the research was also displayed

on the cover page (informed consent form). After accepting the terms, participants could

proceed with the survey. Thus, participation in this study was completely voluntary (see

Section 3.6).

Participation before 7 January 2017 allowed participants to be placed into the draw to win

a $100 Prezzy card. Four cards were available to give away in the first four weeks of the

survey. Participants whose responses were completed received an email announcing the

name of the winner along with a note of appreciation. Participants who did not complete

the survey or had not started it by that time received a reminder email along with the name

of the winner in order to reduce non-response bias (Bryman & Bell, 2015). The survey

closed on 4 February 2017 and responses were automatically recorded by Qualtrics.

3.10 Survey Diagnostics

3.10.1 Response rate and sample profile

From the 922 survey invitations, 330 responses were recorded between December 2016

and February 2017 (a response rate of 35.79%). According to Nulty (2008), most online

surveys achieve an average response rate of 33%, therefore, this study’s response rate

was considered satisfactory.

A substantial minority of the participants (44.4%) were from the airport/passengers

services, 32.1% were ramp and cleaning services, 20.9% were baggage services

employees and 2.5% operations performance services. A total of 63.2% of participants

were part-time employees and 36.8% were full-time. The majority (80.1%) were team

members and 19.9% were managers, support managers, team managers or team leaders.


55

A total of 63.2% were aged between 15 and 44 years while 33.2% were 45 to 64 and 3.6

% were 65 year or older. Most (37.9%) had high school qualification while 36.1% had a

diploma or certificate, 4.3% had no qualification and 21.6% had a bachelor degree or

higher. In terms of years of work experience, 46.9% of the participants had five years of

experience or less, 36.1% had between six and 10 years where, 9.4% had between 11 and

15 years, and finally, only 7.6% had more than 15 years of experience. The majority

(72.6%) were married or in a relationship, 16.2% had never married, 6.5% were divorced,

2.9% were ‘other’ and 1.8% were widowed. There were slightly more male participants

(52.7%) than females (47.7%). Half (50.9%) had no dependent children while 16.6 % had

one and 31.1% had between two and five; 1.6% had more than five children. This sample

provides a good cross-section of respondents across an array of demographics.

3.10.2 Missing data, non- response bias and common method bias

After examining the recorded responses and conducting Little’s Missing Completely at

Random (MCAR) test, we concluded that the data were completely missing at random

(α>.05). Therefore, the Complete-Case Analysis (CCA) approach was used and only fully

answered surveys (278 responses) were included, discarding those with few missing data.

This approach yields unbiased estimates of mean response trends (Nakai & Ke, 2011).

Non-response bias is the expected mistake in estimating a population characteristic based

on a sample of data which is under-represented due to non-response (Berg, 2005). Non-

response bias was tested using the goodness of fit test. All responses prior to 7 January

2017 were considered “early”, whereas, later responses were considered “late”. In

comparing a few demographic variables from both timeframes, we can see that there is

no statistical difference between the variables (p>.05, see Table A1). Therefore, it was

concluded that non-response bias was not a major concern.


56

Common Method Bias (CMB) is a measurement error which happens when an instrument

introduces bias (Roni, 2014). CMB was tested using Harman's single factor test. All

independent variables were loaded into a one common factor. The result was obtained by

running un-rotated factor solution and 1 number of factor. The first component accounts

for less than 50% of the all variables in the model (see Table A2), therefore, common

method bias was not a major concern.

3.10.3 Assumptions testing

Assessing normality of the data

The normality of the data (i.e. its distribution) was analysed first. Normality tests, such

as Kolmogorov-Smirnov (K-S) test and a Shapiro-Wilk (S-W) test, supplemented the

visual inspection from frequency distribution (histograms) or Q-Q plot (Ghasemi &

Zahediasl, 2012).

The total job satisfaction level had a mean score of M=63.09 and standard deviation of

SD=7.716. Reported 5% Trimmed Mean was Mtrimmed=63.37, which was slightly

different from the normal mean score as shown in (Table A3); therefore, the extreme

cases did not pose a significant influence on the mean of the dataset and did not require

further investigation.

The K-S test and S-W test showed that (p<.05) as illustrated in (Table A4). Therefore,

job satisfaction was not normally distributed. However, according to Ghasemi and

Zahediasl (2012), the distribution of the data is not a major problem and can be ignored

if the sample size is greater than 30 or 40.


57

Assessing homogeneity of variances

Homogeneity of variance means that the variance of the outcome variable is equal at all

levels (Field, 2013). This assumption was assessed by using Levene’s F test (Field, 2013).

The results for this test are shown in (Table A5) where F=.895 and p=.678. Since p>.05,

it was concluded that the variance between the three groups was not significantly

different. Therefore, the assumption of homogeneity of variance was met.

Assessing multicollinearity

Multicollinearity is “a situation in which two or more variables are closely linearly

related” (Field, 2013, p. 1013). This assumption was assessed by using Variance Inflation

Factor (VIF) to indicate whether there is a strong linear relationship among the variables

(Field, 2013) and Tolerance, which is the reciprocal of VIF. Results for these tests are

shown in (Table A6). For all risk factors, T>.1 and VIF<10, so there was no

multicollinearity in the data and the assumption of multicollinearity was met.

Test for outliers, normality and linearity (job satisfaction-risk factors)

Outliers have a profound influence on linear regression and it is reasonable to remove

them when they are due to data entry error or “when there are special circumstances

surrounding a specific case” (Sweet & Grace-Martin, 2011, p. 181). Therefore, the

availability of outliers in the data was examined. One outlier was found; further inspection

was required to determine the reason behind this. Since data were directly imported from

Qualtrics, there was no chance of data entry error. We examined if there were any special

circumstances by reading all comments provided by the participant. It emerged that one

respondent faced a “change of circumstances at home and personal commitments” which

made him or her think of “resigning”. The participant was already determined to find a


58

new job; therefore, he or she was experiencing significantly low job dissatisfaction. As a

result of the evidence, the outlier was removed from the data set and 277 responses were

used for analysis. As illustrated in (Figure A1 and Figure A2), the residuals were normally

distributed and had a linear relationship.

3.11 Data Analysis Approach

3.5.1 Quantitative data

Quantitative/quantifiable data were handled and computed using SPSS, which provides

the necessary tools for data storage, data management, modifications and complex

statistical analysis. Coded data were downloaded in a special format from Qualtrics that

is compatible with SPSS. A backup of the data was made and encrypted for safety reasons.

Reverse coding was required as several questions were designed to be negatively worded.

Furthermore, descriptive statistics were used to describe the personal demographic

variables, which include department, employment type, position, age, level of education,

work experience, marital status, gender and number of dependent children respectively.

Inferential statistics; General Linear Model (GLM), Analysis of Variance (ANOVA),

correlation, multiple linear regression were used to answer the research questions. For all

inferential statistical tests, alpha was set at .05. Therefore, any inferential statistical test

with a p-value exceeding .05 indicated no statistical significance.

ANOVA assumptions were assessed by testing for normality and homogeneity of

variances. Regression assumptions were assessed by testing for multicollinearity, outliers,

normality and linearity prior to conducting the analysis (see Section 3.10.3).

GLM was used to find if there is an impact of the personal demographic variables

(department, position, employment type, age, educational level, work experience, marital


59

status and number of dependent children) on job satisfaction. ANOVA test was used to

compare the means of the departments in terms of job satisfaction. In addition, a

correlation test was used to examine the extent of the relationship between the risk factors

(difficulty falling asleep, health issues, stress, fatigue, family conflict and isolation) and

job satisfaction. A hierarchical regression test was used to examine whether or not the

level of job satisfaction (the dependent variable) can be predicted by the risk factors (the

independent variables). The order of entry of the risk factors into the regression model

was based on the outcome of the correlation test between these risk factors and the level

of job satisfaction (Kim, 2016).

3.5.1 Qualitative data

Responses to open-ended questions were analysed using a thematic analysis as it is the

most common approach for qualitative data analysis (Bryman & Bell, 2015). This

approach requires identifying certain phrases or words that frequently occurred within the

data and categorise them into themes (Bryman & Bell, 2015). This can be done manually

to avoid the ambiguity associated with software (Popping, 2015). However, this method

could be challenging as the answers are somewhat unique (Zikmund, 2003). At the

conclusion of the open-ended questions, a number of clear and consistent themes emerged

that corroborated, and none refuted, the quantitative findings and percentages of each

identified concept were calculated in order to enrich interpretation of the quantitative

findings.

3.12 Summary

This study adopted a largely positivist research approach, using a quantitative

methodology as the primary mode of investigation. A qualitative approach supplemented

it in order to gain an understanding of underlying reasons and opinions of participants on


60

certain issues, and to corroborate statistical findings. The study was conducted in the

aviation sector due to it being one of the important sectors that is operational around the

clock and due to the limited availability of research in this area. One case organisation

was explored to provide detailed observations from a cross-sectional survey. This type of

survey was preferred over a longitudinal study as it elicits data that can enable a

comprehensive and robust response to the key research questions. The survey was piloted

before being distributed and data collected via an online survey platform. The survey

included a mixture of open and closed-ended questions. The target population were all

shift workers in the case organisation. All participants were free to withdraw from the

study at any time, participation in the study was completely voluntary and strict

confidentiality was guaranteed. Furthermore, data were handled and computed using

SPSS and several analysis techniques were selected in order to answer the research

questions. A satisfactory survey response rate was attained. The next chapter presents the

results of the study.


61

Chapter 4 – Data Analysis and Results

4.1 Introduction

The previous chapter detailed the methods and data analysis tools used to gather

quantitative and qualitative data at the case organisation. This chapter presents the

statistical and qualitative results from analyses of both categorical and open-ended

responses to the survey in order to answer the research questions. Section 4.2 provides

the contextual survey data. Empirical findings, drawing on both forms of inquiry for the

first and second research questions respectively, are presented in Section 4.3 and 4.4.

Quantitative and qualitative data also provide empirical evidence for analysis in relation

to the third research question in Section 4.5. Suggestions from participants are presented

and analysed in Section 4.6. This chapter concluded with a summary in Section 4.7.

4.2 Contextual Survey Data

Descriptive statistics was performed to explore the main reasons as to why employees

tend to work shifts (see Table 4).

Of the nine options being ranked from 1 most important to 9 least important, participants

ranked flexibility in working hours as their first (33.94%) and second reasons (23%).

Gaining higher income was third (16.61%) while fourth was having the option to do

further study (19.61%). The fifth reason was having the freedom in working practice and

pace (16.61%). Training opportunities were sixth (18.4%). However, no supervision from

managers was ranked between seventh (17.33%) and eighth (21.66%). ‘The only job

available’ (43.68%) ranked last.


62

Table 4

Frequency Table of Reasons for Working Shifts

From the qualitative data, we can see that participants have highlighted three main themes

as an advantage of shift work. Firstly, flexibility in working hours due to shift work

provided them with the ability to perform other tasks such as domestic duties and personal

commitments (e.g. a female participant, married/in a relationship, without dependent

children, working part-time as a team member for the airport/passenger services

department said: “the benefits of shift work for me, are not having to ask for time off

during a day for appointments, such as dental, medical, hair etc. Also, not having to travel

in rush hour traffic”). Secondly, the positive contribution of shift work to work-life

balance (e.g. a female participant, married/in a relationship, without dependent children,

working part-time as a team-member for the airport/passengers services department

commented: “shift work had increased my job satisfaction as it has given me greater

work life balance and I now have more time to do things I enjoy outside of work without

taking a huge financial cut to do so”). Lastly, ability to study and/or have another part-

time job (e.g. a male participant, married/in a relationship, without dependent children,

working part-time as a team-member for the ramp and cleaning services department said:

Reasons Rank Frequency Percentage

Flexibility in working hours 1 94 33.94

Flexibility in working hours 2 64 23

Higher income 3 46 16.61

Having the option to do further study 4 53 19.13

Having freedom in working practice and pace 5 46 16.61

Training opportunities 6 51 18.4

No supervision from managers 7 48 17.33

No supervision from managers 8 60 21.66

It was the only job available 9 121 43.68


63

“Shift hours should be fixed to suit sleeping patterns and also give us (part timers) a

chance to take up other things such as, another part time job or study outside those

hours”).

Descriptive statistics were also used to assess whether employees felt that the benefits of

working shifts outweighed their drawbacks (see Table 5).

Table 5

Frequency Table of Benefits Outweigh the Drawbacks of Working Shifts

Frequency Percent

Strongly agree 10 3.6

Agree 31 11.2

Neither agree or disagree 123 44.4

Disagree 91 32.9

Strongly disagree 22 7.9

Total 277 100

Around 44.4% were neutral regarding whether the benefits of working shifts outweighed

drawbacks. However, 32.9% disagreed and 7.9% strongly disagreed with the statement.

A further 11.2% agreed and 3.6% strongly agreed with the statement.

4.3 Empirical Findings for the First Research Question

The first research question asked: Does job satisfaction vary according to personal

demographic variables? To respond to this research question, a GLM test was applied to

the survey data to test whether or not the difference between the means among the

personal demographic variables was significant. ANOVA and Post Hoc test were applied

whenever the difference in means among the independent groups was found to be

statistically significant.


64

Table 6 shows the results of the analysis of variance test in terms of job satisfaction.

Among the personal demographic variables, the difference in means between departments

was statistically significant (F=4.166, df=3, p=.007). This was evidenced by the p-value

being less than the alpha level of .05. However, the difference in means among

employment type, position, age, level of education, work experience, marital status,

gender and number of dependent children was not statistically significant as the p-value

for all factors was greater than the alpha level of .05.

Table 6

General Linear Model (Tests of Between-Subjects Effects)

Source

Type III Sum

of Squares df Mean Square F Sig.

Corrected Model 2156.551a 33 65.350 1.183 .236

Intercept 19382.824 1 19382.824 350.774 .000

Department 690.531 3 230.177 4.166 .007

Employment Type 5.892 1 5.892 .107 .744

Position 25.242 1 25.242 .457 .500

Age 225.923 5 45.185 .818 .538

Level of education 233.989 4 58.497 1.059 .378

Work Experience 38.963 4 9.741 .176 .950

Marital Status 144.576 4 36.144 .654 .625

Gender 64.585 2 32.293 .584 .558

Dependent Children 427.744 9 47.527 .860 .562

Error 13427.529 243 55.257

Total 1121938.000 277

Corrected Total 15584.079 276

a. R Squared=.138 (Adjusted R Squared=.021)


65

Table 7

ANOVA (in Terms of Departments)

An ANOVA test was applied for the variable shown to be significant (p<.05), department.

The results showed that the difference in means between departments was statistically

significant (F=4.796, df=3, p=.003; see Table 7).

Furthermore, Tukey’s HSD was used for post-hoc analysis. The test had tight control over

Type I error rate and good power (Field, 2013). As shown by Table 8, only ramp and

cleaning services and airport/passengers services had a significant mean (p<.05).

The qualitative data revealed that, out of 123 comments, employees working in ramp and

cleaning services and airport/passengers services departments experience task variety

(42.3%) and challenge (16.3%). They also find the job interesting and it allows them to

learn new things (29.2%). A small remainder (12.2%) provided irrelevant commentary

(e.g. one respondent asked, “ WOW!, is this even a real survey question (I hope our HR

Team have reviewed the surveyor’s questions before investing money into this outsourced

company?)”).

Based on the above analysis, we can conclude that job satisfaction varies according to

certain demographic variables: department.

Sum of Squares df Mean Square F Sig.

Between Groups 780.232 3 260.077 4.796 .003

Within Groups 14803.848 273 54.227

Total 15584.079 276

JOB

SATI

SFA

CTI

ON

OF

SHIF

T W

OR

KER

S IN

TH

E N

Z A

VIA

TIO

N S

ECTO

R

66

Tabl

e 8

Tuke

y H

SD M

ultip

le C

ompa

riso

ns

(I) D

epar

tmen

t (J

) Dep

artm

ent

Mea

n D

iffer

ence

(I-J

)

Std.

Erro

r Si

g.

95%

Con

fiden

ce In

terv

al

Low

er B

ound

U

pper

Bou

nd

Ram

p an

d cl

eani

ng se

rvic

es

Bag

gage

serv

ices

-.3

65

1.24

3 .9

91

-3.5

8 2.

85

Airp

ort /

pas

seng

ers s

ervi

ces

-2.9

67*

1.02

5 .0

21

-5.6

2 -.3

2

Ope

ratio

ns P

erfo

rman

ce

-7.3

82

2.89

1 .0

54

-14.

85

.09

Bag

gage

serv

ices

R

amp

and

clea

ning

serv

ices

.3

65

1.24

3 .9

91

-2.8

5 3.

58

Airp

ort /

pas

seng

ers s

ervi

ces

-2.6

03

1.17

3 .1

21

-5.6

3 .4

3

Ope

ratio

ns P

erfo

rman

ce

-7.0

17

2.94

6 .0

83

-14.

63

.60

Airp

ort /

pas

seng

ers s

ervi

ces

Ram

p an

d cl

eani

ng se

rvic

es

2.96

7* 1.

025

.021

.3

2 5.

62

Bag

gage

serv

ices

2.

603

1.17

3 .1

21

-.43

5.63

Ope

ratio

ns P

erfo

rman

ce

-4.4

15

2.86

1 .4

13

-11.

81

2.98

Ope

ratio

ns p

erfo

rman

ce

Ram

p an

d cl

eani

ng se

rvic

es

7.38

2 2.

891

.054

-.0

9 14

.85

Bag

gage

serv

ices

7.

017

2.94

6 .0

83

-.60

14.6

3

Airp

ort /

pas

seng

ers s

ervi

ces

4.41

5 2.

861

.413

-2

.98

11.8

1

*. T

he m

ean

diff

eren

ce is

sign

ifica

nt a

t the

.05

leve

l.

Dep

ende

nt V

aria

ble:

Job

Satis

fact

ion


67

4.4 Empirical Findings for the Second Research Question

The second research question asked: What is the nature and extent of the relationship

between job satisfaction and the risk factors?

A correlation test was applied (see Table 9). The relationship between job satisfaction

and each risk factor was significant (p <.05) at the .05 alpha level (2-tailed). The nature

of these relationships was negative. This means that, as each risk factor increases, the

level of job satisfaction decreases and vice versa. According to the rule of thumb, there

was a low to moderate relationship between job satisfaction and stress, difficulty falling

asleep and isolation. Furthermore, there was a moderate to substantial relationship

between job satisfaction and health issues, fatigue and family conflict (De Vaus, 2002).

Four inter-related themes were extrapolated and developed from the open-ended

questions which supported the statistical findings: issues with sleeping patterns; stress;

fatigue; and family conflict.

4.3.1 Issues with sleeping patterns

Approximately 11% of the 140 comments indicated that respondents are experiencing

issues with sleeping patterns. A male respondent, married/in a relationship, with four

dependent children, working full-time as a manager for the ramp and cleaning services

department said: “I find it’s hard on the body and on sleep patterns”. Several respondents

said that issues with sleeping are due to the consistent change in the schedule. One male,

never married, without children and working full-time as a team member for the ramp

and cleaning services said: “schedule changes affecting sleep patterns”. Furthermore, one

respondent looked at the issue as a commonly-experienced problem: “Shifts start and

finish times are all over the place. We work 3 early and 3 late shifts. We should go back


68

to 4 on 4 off, 12 hr. days. In the mornings, we could start as early as 3.30am and on the

lates finish at 3am. This isn't good for our sleep patterns. We all have sleep problems”

(male, married/in a relationship, with three dependent children, full-time manager for the

operations performance department).

4.3.2 Stress

Approximately four percent of the comments revealed that the respondents’ jobs were

stressful and difficult. A female participant, divorced, without dependent children,

working part-time as a team member for the airport/passengers services department,

stated that stress is due to the working hours: “I that find shift work puts a lot of stress on

the body.” Others reported that stress is due to the need of meeting flights departure

schedules. A male participant, married/in a relationship, with no dependent children, who

works part-time as a team member for the baggage services department said: “Due to the

high physical work and stress to meet the deadlines of the flights departure. However,

having a great team makes a big difference.” Furthermore, stress could be experienced

due to passengers’ attitudes or behaviour. For instance, a female participant, married/in a

relationship, with four dependent children, working full-time as a manager for the

baggage services department, reported: “with pressure from the environment and

sometimes the nature of people we deal with it can get extremely stressful.”

JOB

SATI

SFA

CTI

ON

OF

SHIF

T W

OR

KER

S IN

TH

E N

Z A

VIA

TIO

N S

ECTO

R

69

Tabl

e 9

Cor

rela

tion

betw

een

Job

Satis

fact

ion

and

the

Risk

Fac

tors

Jo

b Sa

tisfa

ctio

n

Diff

icul

ty

Falli

ng A

slee

p H

ealth

Issu

es

Stre

ss

Fatig

ue

Fam

ily

Con

flict

Is

olat

ion

Job

Satis

fact

ion

Pear

son

Cor

rela

tion

1

Diff

icul

ty F

allin

g A

slee

p Pe

arso

n C

orre

latio

n -.2

88**

1

R

.0

829

Hea

lth Is

sues

Pe

arso

n C

orre

latio

n -.3

63**

.3

58**

1

R

.1

318

Stre

ss

Pear

son

Cor

rela

tion

-.295

**

.403

**

.490

**

1

R

.0

87

Fatig

ue

Pear

son

Cor

rela

tion

-.344

**

.486

**

.460

**

.535

**

1

R

.1

183

Fam

ily C

onfli

ct

Pear

son

Cor

rela

tion

-.317

**

.271

**

.390

**

.439

**

.334

**

1

R

.1

005

Isol

atio

n Pe

arso

n C

orre

latio

n -.2

45**

.3

15**

.3

93**

.4

00**

.4

55**

.5

23**

1

R

.0

6

**. C

orre

latio

n is

sign

ifica

nt a

t the

.01

leve

l (2-

taile

d).

N=2

77

R=r

2


70

4.3.3 Fatigue

Approximately 12% of the comments highlighted that respondents felt fatigued and/or

tired. However, they perceived that this was due to the nature of the job. A male

participant, married/in a relationship, without dependent children, working full-time as a

team member for the ramp and cleaning services department said: “sometimes feels tired

and fatigue but someone has to do it”. Few tended to work extra hours to gain extra

income, as working very early/late shifts results in a higher pay rate, but thus felt more

tired. A female participant, married/in a relationship, without dependent children,

working part-time as a team member for the airport/passengers services department said:

“to earn more money you need to work either very early or very late and that contributes

to fatigue.”

4.3.4 Family conflict

Approximately 11% of the comments indicated that shift work provided them with more

time for family responsibilities and to care of their children. One female respondent,

married/in a relationship, without dependent children, working full-time as a team

member for the ramp and cleaning services department, reported that shift work “helps

with childcare management and children who are still at school”. However,

approximately nine percent reported that it had an impact on their family. Respondents

found it hard to attend family functions as most were held during the weekends. A male

participant, married/in a relationship, with three dependent children and working as a full-

time manager for the ramp and cleaning services department stated, “shiftwork (full-time)

affects your family. Not being able to attend birthdays, having time off with them during

weekends or school events, and coming back home tired after a difficult day.”

Furthermore, other workers reported that shift work had an impact on their family


71

relationships. A female participant, married/in a relationship, without dependent children

and working part-time as a team member for the airport/passenger services department,

mentioned that “it really does affect to relationship and family situations”. Another said:

“my wife does shiftwork too, so that we both work around our children this means our

married lives and religious commitments are more than compromised” (male, married/in

a relationship, with three dependent children, full-time manager for the airport/passengers

services department).

Based on the above analysis, we can therefore evidence a relationship between job

satisfaction and the risk factors.

4.5 Empirical Evidence for the Third Research Question

The third research question asked: Can job satisfaction be predicted by the risk factors?

This question examines the impact of the risk factors on the level of job satisfaction.

Multiple regression analysis was used, the results of which are shown in Table 10 to 13.

Table 10 shows that the dependent variable in this multiple regression analysis was job

satisfaction and the predictor (independent) variables were the risk factors. The method

of entering the predictor variables into the regression model was the hierarchical method,

where the order of entry was based on the extent and significance of the relationship

between the risk factors and job satisfaction (Field, 2013). Since the predicted variables

correlated with one another, hierarchical regression was an appropriate tool (Lewis,

2007).

Furthermore, according to Table 9, the correlation between health issues, fatigue and

family conflict and job satisfaction was statistically significant. Health issues had the

strongest relationship with job satisfaction (R-square=13.18%), followed by fatigue (R-


72

square=11.83%) and family conflict (R-square=11.97%). However, the correlation

between stress and job satisfaction, followed by difficulty falling asleep and isolation was

not statistically significant. Thus, as shown in Table 10, the risk factors were entered into

the regression model one by one in the following sequence: health issues, fatigue, family

conflict, stress, difficulty falling asleep and finally isolation.

Table 10

Hierarchical Method of Entry for the Predictor Variables (Variables

Entered/Removeda)

Table 11 illustrates that the first, second and third models were statistically significant

while the fourth, fifth and sixth models were not. Finally, based on the results, it is evident

that the only significant predictor variables in this analysis were health issues, fatigue and

family conflict.

Table 12 indicates the overall significance of the regression models. According to Norusis

(2006), the ANOVA in multiple regression tests whether or not there is a linear

relationship between the predictor (independent) variables and the dependent variable.

The relationship between the level of job satisfaction and the risk factors was found to be

linear, as evident from the p-values of the six models which were less than .05.

Model Variables Entered

Variables

Removed Method

1 Health Issuesb . Enter

2 Fatigueb . Enter

3 Family Conflictb . Enter

4 Stressb . Enter

5 Difficulty Falling Asleepb . Enter

6 Isolationb . Enter

a. Dependent Variable: Job Satisfaction

b. All requested variables entered.


73

Table 11

Model Summary of the Multiple Regression Analysis

Mode R

R

Square

Adjusted

R

Square

Std. Error

of the

Estimate

Change Statistics

R

Square

Change

F

Change df1 df2

Sig. F

Change

1 .363a .132 .128 7.015 .132 41.687 1 275 .000

2 .414b .171 .165 6.866 .040 13.101 1 274 .000

3 .442c .196 .187 6.776 .024 8.251 1 273 .004

4 .443d .196 .184 6.787 .000 .139 1 272 .709

5 .451e .203 .189 6.769 .007 2.464 1 271 .118

6 .451f .204 .186 6.780 .000 .152 1 270 .697

a. Predictors: (Constant), Health Issues

b. Predictors: (Constant), Health Issues, Fatigue

c. Predictors: (Constant), Health Issues, Fatigue, Family Conflict

d. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress

e. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress, Difficulty

Falling Asleep

f. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress, Difficulty

Falling Asleep, Isolation

Table 13 shows the coefficients values for each predictor variable in the multiple

regression model which included the unstandardized coefficients β and the standardized

coefficients Beta. According to Norusis (2006), β coefficients can be used to write the

estimated regression equation for the model. The negative coefficient shows that the

predicted value of the dependent variable decreases when the value of the independent

variable increases. The level of job satisfaction was predicted only by three significant

variables: health issues, fatigue and family conflict. However, the aim of the third

research question was to examine if the level of job satisfaction could be predicted by the


74

risk factors. Therefore, Table 13 is discussed in terms of Model 6 because it includes

these factors.

The table shows that, based on the coefficients of β and beta, the regression equation for

the three significant predictor variables was PLJS=[76.332–(1.645×health issues)–

(1.200×fatigue)–(1.338×family conflict)]. Accordingly, the predicted level of job

satisfaction (PLJS) increases as the value of health issues (B=-1.645), fatigue (B=-1.200)

and family conflict (B=-1.338) decreases.

Based on the above analysis, we can deduce that job satisfaction can be predicted by three

risk factors (health issues, fatigue and family conflict).


75

Table 12

The Overall Significance of the Multiple Regression Model (ANOVAa)

Model Sum of Squares df Mean Square F Sig. 1 Regression 2051.423 1 2051.423 41.687 .000b

Residual 13532.657 275 49.210 Total 15584.079 276

2 Regression 2668.934 2 1334.467 28.311 .000c

Residual 12915.146 274 47.136 Total 15584.079 276

3 Regression 3047.834 3 1015.945 22.124 .000d

Residual 12536.246 273 45.920 Total 15584.079 276

4 Regression 3054.254 4 763.563 16.576 .000e

Residual 12529.826 272 46.066 Total 15584.079 276

5 Regression 3167.152 5 633.430 13.825 .000f

Residual 12416.928 271 45.819 Total 15584.079 276

6 Regression 3174.140 6 529.023 11.510 .000g

Residual 12409.939 270 45.963 Total 15584.079 276

a. Dependent Variable: Job Satisfaction

b. Predictors: (Constant), Health Issues

c. Predictors: (Constant), Health Issues, Fatigue

d. Predictors: (Constant), Health Issues, Fatigue, Family Conflict

e. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress

f. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress, Difficulty Falling Asleep

g. Predictors: (Constant), Health Issues, Fatigue, Family Conflict, Stress, Difficulty Falling Asleep, Isolation


76

Table 13

The Contribution Measurements of the Predictor Variables to the Multiple Regression

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 70.968 1.275 55.661 .000

Health Issues -3.088 .478 -.363 -6.457 .000

2 (Constant) 74.258 1.544 48.098 .000

Health Issues -2.210 .527 -.260 -4.193 .000

Fatigue -1.751 .484 -.224 -3.619 .000

3 (Constant) 75.494 1.583 47.677 .000

Health Issues -1.770 .542 -.208 -3.263 .001

Fatigue -1.488 .486 -.190 -3.060 .002

Family Conflict -1.347 .469 -.172 -2.872 .004

4 (Constant) 75.652 1.641 46.102 .000

Health Issues -1.716 .562 -.202 -3.054 .002

Fatigue -1.416 .523 -.181 -2.706 .007

Family Conflict -1.300 .486 -.166 -2.673 .008

Stress -.229 .612 -.026 -.373 .709

5 (Constant) 76.361 1.698 44.976 .000

Health Issues -1.622 .564 -.191 -2.878 .004

Fatigue -1.150 .549 -.147 -2.095 .037

Family Conflict -1.260 .486 -.161 -2.594 .010

Stress -.103 .616 -.012 -.167 .867

Difficulty Falling Asleep -.723 .461 -.100 -1.570 .118

6 (Constant) 76.332 1.702 44.847 .000

Health Issues -1.645 .568 -.193 -2.898 .004

Fatigue -1.200 .565 -.154 -2.125 .034

Family Conflict -1.338 .526 -.171 -2.543 .012

Stress -.112 .617 -.013 -.182 .856

Difficulty Falling Asleep -.732 .462 -.101 -1.584 .114

Isolation .209 .535 .027 .390 .697


77

4.6 Suggestions From Participants

The participants also provided suggestions about shift working. A total of 44% suggested

that it could be fixed or a constant shift instead of rotating. This would enable them to

better plan their family and social life (e.g. a male respondent, married/in a relationship,

without dependent children, working part-time as a team member for the ramp and

cleaning services department said: “Shift hours should be fixed to suit sleeping patterns

and also gives us (part timers) a chance to take up other things such as, another part time

job or study outside those hours”). A total of 35.9% suggested making amendments to

the shift work rota, including extending working hours, following only one type of shift

for example; either morning, evening, or night shift according to their personal preference

and family obligations, changing the start and end time of shifts (e.g. a female respondent,

never married, without dependent children, working part-time as a team member for the

ramp and cleaning services said: “sometimes I feel the 6 days are too long. I think that I

would prefer 4 on 2 off or something of that sort, the last few days are often very tiring

and hard mentally to focus and be happy”).

A group of participants (12.8%) were concerned with their health and well-being,

suggesting the provision of healthy meal plans, a gym, power naps and counselling (e.g.

a female respondent, married/in a relationship, with four dependent children, working

full-time as a manager for the baggage services department said: “I think with the nature

of the job we should be a lot to take power naps on our breaks…We have been stopped

due to the fact that it looks unprofessional to be sleeping on your break. I think if it helps

your mind be alert your focus will be better at work, and we should really be judged for

what you do on our break”). Another participant said: “More communication to

employees regarding their daily life difficulties doing shift work” (male, married/in a


78

relationship, no dependent children, part-time team member for the baggage services

department).

4.7 Summary

This chapter presented the research quantitative and qualitative results. Quantitative

results were conveyed using descriptive and inferential statistics, while qualitative results

were presented thematically, using direct speech and narrative. The majority of the

participants were male part-time employees, aged between 25 and 44 years, holding a

high school qualification, having five years’ work experience or less, married/in a

relationship and without dependent children. Working shifts provided employees with

flexibility in working hours and high income, making it the most important reason for

employees to undertake the job regardless of its drawbacks. Almost an equal proportion

of participants who were either neutral or disagreed/ strongly disagreed with the benefits

of working shifts outweighing its drawbacks.

The findings revealed that job satisfaction does not vary according to the personal

demographic variables; there was no relationship between employment type, age,

educational level, work experience, marital status, gender, number of children and job

satisfaction. However, there was a significant difference in means between department

type and job satisfaction. Further analysis revealed that, among the four departments, only

ramp and cleaning services and airport/passengers services departments had significant

means. This indicates that these two particular departments have a significant impact on

job satisfaction. Moreover, there was a negative (low to moderate) correlation between

job satisfaction and stress, difficulty falling asleep and isolation. Furthermore, there was

a negative (moderate to substantial) correlation between job satisfaction and health issues,

fatigue and family conflict.


79

The qualitative data supported and extended the quantitative findings. Health issues,

fatigue and family conflict were found to be the most influential risk factors to negatively

impact on the level of job satisfaction. In addition, majority of the participants have

suggested to have a fixed or constant shift instead of a rotating shift. The following

chapter summarises and discusses the key results with regard to their implications for

theory, policy and practice, as well as the study’s key contributions, limitations and areas

for future research.


80

Chapter 5 – Discussion and Conclusion

5.1 Introduction

The purpose of this study was to explore the impact of demographic variables and risk

factors on the job satisfaction of shift workers in the aviation sector in New Zealand. The

study purpose was underpinned by three research questions. Responses to these questions

were sought through mainly quantitative and some qualitative empirical research. This

chapter seeks to locate the key findings of the study in context and to evaluate their wider

implications. It begins by restating the purpose of the study, reviewing the key findings

in relation to the research questions and existing literature in Section 5.2 and 5.3

respectively. Section 5.4 discusses its implications for developing theory, workplace

practice and policy. Section 5.5 considers the contributions of the study. Potential

limitations which could inform the focus of future studies are discussed in Section 5.6.

This chapter ends with providing a summary and final remarks.

5.2 Purpose of the Study

It is critical for organisations to understand the factors which influence job satisfaction

among shift workers in order to improve and increase employee well-being and

productivity. Employees are required to work round-the-clock in the aviation sector as

they play a vital role in the performance of the organisation and happy employees tend to

contribute to a more highly performing organisation. This is achieved through quality

service provided to the organisation’s customers, which in turn enhances customer loyalty

and satisfaction (Rast & Tourani, 2012). Many researchers (e.g. Conway et al., 2008;

Jaradat et al., 2017) have studied the impact of shift work on job satisfaction and other

employee related variables. However, research on shift workers in the New Zealand

aviation sector is limited and an investigation was needed to further scholarly and


81

practitioner understanding of job satisfaction in this context. This study thus addressed

three main research questions:

1. Does job satisfaction vary according to personal demographic variables?;


risk factors?; and


5.3 Significant Findings

5.3.1 Job satisfaction and personal demographic variables

This research explored whether job satisfaction varies according to demographic

variables in the New Zealand aviation sector. The literature review revealed that the key

variables of interest were department, position, employment type, age, educational level,

work experience, marital status, gender and family conflict. Quantitative data analysis

revealed several inconsistencies with existing research findings.

Among the demographic variables examined here, only the degree of influence between

job satisfaction and an employee’s department was found to be statistically significant.

This could be due to the difference in the nature of work and task variety among each

department. Previous studies demonstrated that the nature of work plays a vital role in

influencing an individual’s level of job satisfaction, with challenging jobs with a variety

of work often resulting in a higher level of job satisfaction (Locke, 1976) and enabling an

employee to increase their knowledge (Hackman & Oldham, 1979).

The airport/passenger services and ramp and cleaning services departments were the only

two departments with significant means. This means that job satisfaction varies according


82

to the settings/work environment in these two departments. The qualitative findings

extended the quantitative findings, with participants reflecting that the two departments

include a variety of challenges and task variety, thus allowing to learn new things daily.

This outcome supports extant findings which assert that there is a positive relationship

between job satisfaction and nature of work (Kessuwan & Muenjohn, 2010; Goswami et

al., 2011).

5.3.2 Risk factors and job satisfaction

The correlation analysis indicated that the risk factors (stress, health issues, difficulty

falling asleep, fatigue, family conflict and isolation) had a statistically significant negative

correlation with job satisfaction. This relationship supports previous research which

shows that job satisfaction decreases as each risk factor increases and vice versa.

Analysis of the qualitative data found further support for the quantitative results which

showed that stress was a result of shift work and the enviromental pressures associated

with it, as well as relationships with colleagues and customer attitude. This study’s results

confirm Iqbal and Waseem’s (2012) finding of a negative correlation between the two

variables among Air Traffic Controllers working shifts. It also supports Kula (2017) and

Zeytinoglu et al. (2007) who found a significant negative relationship between stress and

job satisfaction among shift workers.

Researchers such as Conway et al. (2008) found that work-related stress is highly relevant

to poor health. This study concluded that health issues have a moderate to substantial

negative correlation with job satisfaction. Most researchers have focused on the impact

of job satisfaction on employee health issues. However, few have measured the impact of

health issues on job satisfaction (Shields, 2006). This study thus contributed to filling this

research gap, finding that as the physical and mental well-being of shift workers


83

ultimately leads to job dissatisfaction, this impacts on the organisational performance,

commitment and withdrawal behaviours.

Poor sleep quality is confirmed with this research and supported by previous studies such

as Conway et al. (2008) and Karagozoglu and Bingöl (2008) where a low to moderate

negative correlation between sleep quality and job satisfaction among shift workers was

identified. As poor sleep quality increases, a decrease is evident in the level of job

satisfaction. It is apparent that poor sleep quality further results in fatigue. In this study,

fatigue was found to have a moderate to substantial negative correlation with job

satisfaction. This result confirms the findings of Choi and Kim (2013) and Shah and Nina

(2009) where a significant negative correlation between fatigue and job satisfaction was

obtained. The qualitative findings have shown that employees experience fatigue mainly

due to very early or late starts to their shift.

Several studies have proved that shift work leads to family conflict, in turn leading to

poor job satisfaction (Glaveli et al., 2013; Spector, Allen et al., 2007; Stains & Pleck,

1984; Costa, 1997). This is supported by the results here, confirming a moderate to

substantial negative correlation between family conflict and job satisfaction. The

qualitative findings showed equal feeling for shift working facilitating family life and for

not doing so. Few supported the views of Costa (1996, 1997), Shen and Dicker (2008)

and Stains and Pleck (1984) who argue that shift work leads to low level of family

adjustments, poor attendance at school events and family gatherings. However, others

supported the claims of others such as Demerouti et al. (2004) who saw shift work as an

opportunity which provides flexibility and additional time for family responsibilities and

caring for their children. Furthermore, isolation was identified to have a low to moderate

negative correlation with job satisfaction, confirming the results of previous studies (e.g.


84

Dhawan, 2015). The value of the qualitative data was evident as they provided further

insights into the reasons for the inverse relationship.

5.3.3 Predictors of job satisfaction

The multiple regression analysis indicated that health issues, fatigue and family conflict

predict job satisfaction and contribute significantly in terms of understanding the

relationship in the regression model. In addition, the results identified that, within the case

organisation, the level of health issues, fatigue and family conflict can be an effective

element for estimating the level of job satisfaction of shift workers. It is surprising to find

that poor sleep quality was not one of the main predictors as extant research observes it

to be most recognized problem among shift workers as irregular working hours results in

an inadequate sleeping pattern which the body requires to function effectively (Åkerstedt,

2003; Costa, 1997; Monk & Folkard, 1992). One possible explanation is that shift workers

in the case organisation viewed other factors as more important than poor sleep quality.

This finding is consistent with Shields (2006) who notes that health issues have an impact

on job satisfaction and Choi and Kim (2013) and Shah and Nina (2009) who found that

fatigue impacts on an employee’s job satisfaction. Furthermore, Glaveli et al. (2013) and

Gozukara and Çolakoğlu (2015) have observed that family conflict impact on employee

job satisfaction.

5.4 Implications of the Key Study Findings

5.4.1 Developing theory

The majority of the few available studies in the sector have looked at how organisation-

related variables impact on the level of job satisfaction among aviation employees. This

study extends on the focus of the demographics and the risk factors related to shift work.


85

Therefore, job satisfaction can be viewed from an entirely different perspective, as

existing studies have focused on organisational variables such as pay, opportunities for

promotion, supervision, relationship with co-workers and the work environment

(Mohanty, 2016; Rast & Tourani, 2012). Figure 4 illustrates the conceptual framework

which has emerged from the key findings of this study. Among the demographic

variables, the bold variable (nature of work) was found to be significant whereas among

the risk factors, only the bold variables (health issues, family conflict and fatigue) were

found to be predictors of job satisfaction. The dotted line arrow illustrates a low to

moderate (difficult falling asleep, stress and isolation) and moderate to substantial

(health issues, family conflict and fatigue) relationships with job satisfaction.

Figure 4. Developed Conceptual Framework

With regard to the main job satisfaction theories outlined earlier (see Section 2.2.2), the

key findings would suggest that equitable practices within and across departments are

important antecedents of shift workers’ job satisfaction level. Although Herzberg’s two-

factor theory states that the absence of the motivating factors do not necessarily lead to

job dissatisfaction, this study reveals that work itself (one of the motivators) has a





Risk Factors Stress



Isolation



86

significant impact on the level of job satisfaction of shift workers. In line with the third

model on job characteristics, a challenging and interesting job, requiring the use of

various skills, is likely to lead to higher job satisfaction. These findings pertain to a single

case organisation in a particular sector; thus their generalisability would require further

study (see Section 5.6)

5.4.2 Workplace practice

The findings also help to inform HRM by highlighting how the variety and challenges

in a job can lead to an increase in the level of job satisfaction among shift workers. The

aviation sector has the potential to increase job satisfaction, employee retention and

productivity by applying the research findings. For example, an organisation can seek to

enhance productivity and workforce utility by training shift working employees in

multiple roles. Furthermore, an organisation which is more involved in its employees’

activities (e.g. family social programmes such as, get-togethers, sports and game

activities, arranged by the organisation), given the findings, is likely to increase their job

satisfaction and reduce post-work pressures on staff. Reduced job satisfaction may link

to post-work stressors which can cause family conflict; thus, finding ways to reduce

these stressors will benefit the organisation and its performance. This encourages a more

holistic approach to employee well-being and creates new ways of interacting with

employees which can deepen relationships and enhance organisational culture.

The HR literature suggests that there are best practice and best fit considerations that

organisations can engage with (e.g. Boxall and Purcell, 2016). With regards to best

practice, this study highlighted an area with regards to shift working that can be

addressed (e.g. during induction, the employer could make new shift workers aware of

the challenges associated with their work and offer guidance on managing or minimising


87

them). On best fit, tailored initiatives that reflect an organisation’s specific departmental

characteristics could help their respective worker groups to experience higher job

satisfaction (e.g. task rotation may work better for shift workers in certain departments

due to maintaining their interest and engagement with their work).

5.4.3 Developing policy

External and workplace policy makers have an important role and a significant

responsibility to ensure the safety and satisfaction of employees. It is vital that they are

aware of and familiar with the key effects of shift work on an employee’s level of job

satisfaction, both for the benefit of work organisations and their staff (relations). Since

health issues, fatigue and family conflict were found to be three key predictors of job

satisfaction, policies around health plans (e.g. free medical insurance policy), shift

schedules (e.g. fixed schedules, allowing two complete days off after 18 hours period

over three successive days, and no more than three or four consecutive night shifts) and

performance appraisal and reward systems (e.g. vouchers, family movie/event tickets)

should reflect this so as to improve the level of job satisfaction, worker performance and

organisational productivity. Furthermore, induction policies could assist with preventing

employees from facing difficulties associated with shift work. By developing health and

safety policies, the negative effect of primary risk factors (health issues, fatigue and

family conflict) decrease and the level of job satisfaction increases. Furthermore,

organisations that take care of their shift workers could attract and better engage and

retain staff (i.e. act as ‘employer of choice’).

5.5 Study Contributions

Firstly, this study contributes to the extension of job satisfaction literature as it provides

the first study of job satisfaction among shift workers within the New Zealand aviation


88

sector and extends existing scholarly and practitioner understanding of the demographic

variables and primary risk factors that impact the level of job satisfaction of such workers.

Second, the research addresses for the first time the nature and extent of the relationship

between job satisfaction and the risk factors experienced by shift workers in the aviation

sector in order to pay closer attention to the main factors that affect shift workers job

satisfaction, with implication for employee performance and organisational productivity.

Third, this study highlights the considerable value of utilizing a primary research

approach and supplementing it with another so as to corroborate or refute statistical

findings, as well as elicit deeper insights into the relationships under examination.

Finally, the study makes a key contribution to improving workplace policies and practices

related to shift working employees within the aviation sector, and potentially, beyond. In

particular, the study examined and derived three key predictors of job satisfaction (health

issues, fatigue and family conflict). Their identification is useful, for example, for

employers seeking to augment shift workers’ job satisfaction by focusing their efforts and

initiatives on these key areas. In addition, survey data also provided unique benchmark

material for other organisations in the sector who choose to examine the job satisfaction

of their shift workers.

5.6 Limitations and Future Research

This research looked at job satisfaction among shift workers in the New Zealand aviation

sector. The research was carefully conceived and structured, and as noted above, it

produced a number of contributions in terms of extending knowledge about shift working

and job satisfaction, with regard to theoretical use and practical application of the

findings.


89

However, several study limitations were identified during the research process. It

emerged that the value of integrating quantitative methods with elements of qualitative

methods was important, as qualitative analysis supported the exploration of the variables

which impact on the level of job satisfaction. However, time constraints meant that this

research needed to be cross-sectional in nature in order to explore the impact of the

demographic variables and risk factors on job satisfaction of shift workers in the aviation

sector. A longitudinal study might yield further insights and identify changes in the

characteristics of the participants and their working conditions over time which would

affect their job satisfaction, and potentially, work performance. Moreover, the research

was based on a single case study, limiting the extent to which its results can be

generalised. That said, it may provide a benchmark for subsequent inquires, and these

could involve multiple case studies to provide more robust findings (in terms of sectoral

representation) and a better understanding of the similarities and differences between

different work organisations (Yin, 2014).

In addition, online surveys were the only method of data collection in this study. It was

found that not all employees had access to devices which would enable them to participate

in the survey, possibly due to the nature of their job, their limited technology skills and

other factors, thus contributing to a modest sample size. Future studies might thus include

multiple data collection methods (e.g. a paper-based survey, Computer Assisted Personal

Interviewing (CAPI)) so as to help yield a higher sample size.

Finally, this research was based on a single case organisation. Subsequent work could

look at a wider array of variables at the organisational and environmental levels (e.g. via

a cross-organisational or cross-sectoral survey). A second area of development could

potentially focus on the relationship between job satisfaction and organisation and

employee variables with regard to improving employee performance and organisational


90

productivity. According to numerous studies (e.g. Dhawan, 2015; Davood & Tayebeh,

2012), job satisfaction is negatively related to several organisation-related variables (e.g.

performance, organisational commitment, withdrawal behaviours including absences and

turnover) as well as employee-related variables (e.g. health and life satisfaction)

(Hellman, 1997; Baeriswyl, Krause & Schwaninger, 2016). Furthermore, the relationship

between personal demographic variables and the risk factors could be explored (see

Figure 5 - italicised and bold lines).

JOB

SATI

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ON

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SHIF

T W

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S IN

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Z A

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R

91

Figu

re 5

. Fut

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ork

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es

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Age

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92

5.7 Summary and Final Remarks

This chapter discussed the key findings of the study and their significant implications for

theory, practice and policy. The combination of the quantitative and qualitative results

did not support the expected relationship between job satisfaction and the personal

demographic variables. Only the relationship between job satisfaction and employee

department were found to be statistically significant. Moreover, only two departments

were found to be statistically significant due to the nature of work and task variety. There

was also a weak to moderate inverse relationship between job satisfaction and the risk

factors, with job satisfaction being predicted by health issues, fatigue and family conflict.

These results suggest that there are theoretical developments that could be made in

relation to job satisfaction of shift workers in the aviation sector. The worth of the study

is also reflected in part by its delineation of further, fruitful areas of research, particularly

in the context of shift work in the aviation sector looking to remain a major form of

employment.


93

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Appendices

Appendix A: Access Letter


123

Appendix B: MUHEC Approval Letter


124


125

Appendix C: Survey Invitation


126


127

Appendix D: Online Survey


128


129


130


131


132


133


134


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Appendix E: Email from Manager to Employees


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Appendix F: Survey Diagnostics

Table A1

Early and Late Responses

Variable % n df p

Early Late

Gender Male 72.3 27 144 0.486 2 0.784

Female 71 28.8 132

Age 15 – 24 years 88.5 11.5 26 7.188 5 0.207

25 – 34 years 71.8 28.2 78

35 – 44 years 66.2 33.8 71

45 – 54 years 67.2 32.8 58

55 – 64 years 17.6 82.4 34

65 years or older 70 30 10

Employment Type Full-time 71.6 28.4 102 0.032 1 0.857

Part-time 72.6 27.4 175

Level of Education No qualification 75.0 25.0 12 2.077 4 0.722

High school

qualification 68.6 31.4 105

Diploma/certificate 72.0 28.0 100

Bachelors degree 77.4 22.6 53

Master degree or above 85.7 14.3 7

JOB

SATI

SFA

CTI

ON

OF

SHIF

T W

OR

KER

S IN

TH

E N

Z A

VIA

TIO

N S

ECTO

R

138

Tabl

e A

2

Har

man

's Si

ngle

Fac

tor T

est (

Tota

l Var

ianc

e Ex

plai

ned)

Com

pone

nt

Initi

al E

igen

valu

es

Extra

ctio

n Su

ms o

f Squ

ared

Loa

ding

s To

tal

% o

f Var

ianc

e C

umul

ativ

e %

To

tal

% o

f Var

ianc

e C

umul

ativ

e %

D

epar

tmen

t 2.

437

13.5

40

13.5

40

2.43

7 13

.540

13

.540

Em

ploy

men

t typ

e 2.

175

12.0

81

25.6

21

Po

sitio

n 1.

690

9.38

7 35

.008

Age

1.

577

8.76

3 43

.771

Leve

l of e

duca

tion

1.43

7 7.

982

51.7

53

W

ork

expe

rienc

e 1.

277

7.09

5 58

.847

Mar

ital s

tatu

s 1.

153

6.40

4 65

.251

Gen

der

.883

4.

905

70.1

56

D

epen

dent

chi

ldre

n .8

26

4.58

9 74

.744

Flex

ibili

ty in

wor

king

hou

rs

.820

4.

557

79.3

02

H

ighe

r inc

ome

.732

4.

068

83.3

70

N

o su

perv

isio

n fr

om m

anag

ers

.651

3.

618

86.9

88

H

avin

g fr

eedo

m in

wor

king

pra

ctic

e an

d pa

ce

.609

3.

384

90.3

72

Tr

aini

ng o

ppor

tuni

ties

.539

2.

995

93.3

67

H

avin

g th

e op

tion

to d

o fu

rther

stud

y .5

24

2.91

3 96

.280

It is

the

natu

re o

f the

job

.423

2.

349

98.6

29

It

was

the

only

job

avai

labl

e .2

47

1.37

1 10

0.00

0

Mor

e co

nven

ient

2.

754E

-16

1.53

0E-1

5 10

0.00

0

Extra

ctio

n M

etho

d: P

rinci

pal C

ompo

nent

Ana

lysi

s.


139

Table A3

Descriptive Statistics of Overall Job Satisfaction Score

Statistic Std. Error

Job Satisfaction Mean 63.09 .463

95% Confidence

Interval for Mean

Lower Bound 62.18

Upper Bound 64.00

5% Trimmed Mean 63.37

Median 64.00

Variance 59.541

Std. Deviation 7.716

Minimum 33

Maximum 80

Range 47

Interquartile Range 10

Skewness -.638 .146

Kurtosis .553 .291

Table A4

Test of Normality for Job Satisfaction

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Job Satisfaction .097 278 .000 .974 278 .000

a. Lilliefors Significance Correction


140

Table A5

Test of Homogeneity of Variances (Levene's Test of Equality of Error Variancesa)

Dependent Variable: Job Satisfaction

F df1 df2 Sig.

.895 251 26 .678

Tests the null hypothesis that the error variance of the dependent variable is equal

across groups.

a. Design: Intercept + Department + EmploymentType + Position + Age +

LevelofEducation + WorkExperience + MaritalStatus + Gender +

DependentChildren

JOB

SATI

SFA

CTI

ON

OF

SHIF

T W

OR

KER

S IN

TH

E N

Z A

VIA

TIO

N S

ECTO

R

141

Tabl

e A

6

Mul

ticol

linea

rity

Tab

le

Mod

el

Uns

tand

ardi

zed

Coe

ffic

ient

s

Stan

dard

ized

Coe

ffic

ient

s t

Sig.

95

% C

onfid

ence

Inte

rval

for B

Col

linea

rity

Stat

istic

s

B

Std.

Err

or

Bet

a

Lo

wer

Bou

nd

Upp

er B

ound

To

lera

nce

VIF

1 (C

onst

ant)

77.1

00

1.71

3

44.9

97

.000

73

.726

80

.473

Diff

icul

ty F

allin

g A

slee

p -.7

86

.469

-.1

06

-1.6

75

.095

-1

.711

.1

38

.713

1.

402

Hea

lth Is

sues

-1

.655

.5

77

-.190

-2

.867

.0

04

-2.7

91

-.518

.6

56

1.52

3

Stre

ss

-.271

.6

26

-.031

-.4

34

.665

-1

.503

.9

60

.574

1.

741

Fatig

ue

-1.2

19

.574

-.1

53

-2.1

23

.035

-2

.349

-.0

88

.557

1.

796

Fam

ily C

onfli

ct

-1.3

66

.535

-.1

71

-2.5

55

.011

-2

.419

-.3

13

.644

1.

553

Isol

atio

n .1

70

.544

.0

21

.312

.7

55

-.902

1.

241

.619

1.

615


142

Figure A1. Residuals Histogram

Figure A2. P-P Plot of Regression Standardized Residual

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